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Video Synthesis Workshop with Alex Pelly

How Video Synths Work + Patch Walkthrough

Video synthesis is a mysterious world—one with its own unique logic and scheme of organization. Video synthesizers have their own idiosyncratic, esoteric workflows which can take years to master; and while they do share a lot of common functional parts with more typical audio synthesizers, they can still be difficult for audio synthesists to approach at first.

For instance, a synth enthusiast probably has a good idea what an oscillator does. But what an oscillator does in audio synthesis does not necessarily have an immediate, obvious translation in video synthesis. The function of a filter in a video synth may not do exactly what you'd assume if you approach it from the perspective of a musician; and in fact, transitioning from one of these realms to the other can require a lot of re-programming how you think about your own tools.

Of course, many artists have succeeded in making analog video synthesis their primary means of working. Alex Pelly is one such artist, well-known in LA for creating entrancing and engaging video art and visuals for live shows. Some of her work is featured on Dublab under the series Pellyvision , and we were lucky to have her visit Perfect Circuit to talk about her thoughts about video synthesis. In the video above, Alex breaks down some of the fundamental parts of video synthesis, and walks us through creating a complex, audio-reactive visual patch.

What is a Video Synth?

Video synthesizers allow a user to generate ever-changing visuals in real time—sometimes they are analog, digital, or completely virtual. Often, video synthesizers feature a patchable workflow for generating shapes, for colorizing images, and for completely transforming camera feeds. Video synths were originally designed to create an instrument that would allow visual artists to explore the video medium in real time, rather than working exclusively with pre-recorded and processed footage...much in the way that audio synths were developed to help experimental electronic musicians transition from working with magnetic tape into the realm of realtime creation. So video synths aren't a new concept—they've been around for decades in one form or another, but have recently regained steam amidst the modular synth explosion of the last decade. And while video synths haven't seen quite the intensity of resurgence that audio-based modular systems have enjoyed, it's now not difficult to get started with your own video-oriented modular system.

Thanks to companies like LZX Industries , a vast array of visual opportunities are available in Eurorack format. For years, their Visual Cortex has been the de facto brain of almost all video-based Eurorack systems, and in 2017, their Vidiot standalone instrument provided an easy way for artists to start experimenting with analog video synthesis. Today, LZX's offerings span a fairly huge range: they offer everything from video-optimized utilities and analog signal processors all the way to out-there pattern generators (such as the now-discontinued Diver and Fortress) and advanced digital video processors such as Memory Palace , which harkens back to the days of early Ampex video manipulation systems.

While LZX has recently discontinued their system interface Visual Cortex as well as several of their particularly compelling signal generators/manipulators (such as the Prismatic Ray, Shapechanger, and Navigator), the promise of a new device is just around the corner. We anticipate the release of Chromagnon this fall, a device which promises to meld together several previously disparate video workflows into a single instrument. Developed for use in Eurorack systems or as a standalone device, Chromagnon will work just as well as an introduction to video synthesis as it will an expansion of existing setups. Capable of handling vector rescanning, colorization, shape generation, driving laser displays, and more, Chromagnon is sure to be a blast once it is available.

Despite having the largest selection of video-oriented modules, LZX isn't the only player in the video synth game. The Erogenous Tones Structure , for example, takes a very different approach. An all-in-one digital video synthesis module based on Open GL, Structure is capable of an astonishing range of digital video and animation techniques while retaining the immediacy of a hands-on, voltage-controlled device. Use it to process external signals to to generate video all by itself—Structure is a one-stop shop for insane visual effects, capable of receiving control info via CV, MIDI, and more. Pelly uses one in the above video to add an extra set of visual options to her setup, using integrated feedback and image scaling effects to create profound changes to her patch.

Pelly's presentation is full of excellent technical advice: video synths do have their own unique considerations that we might not think of when planning out an audio synth, and learning their overall workflow can be difficult without a guide. Lucky for us, Alex was able to share a lot of info relevant to anyone looking to get started with video synths, and hopefully was able to clear up some questions about how to get things up and running.

But of course, as with audio synths, the magic isn't all about the gear itself—it's about how you use it. Alex spends a lot of time in her video talking about ways of harnessing feedback, ways of relating visual changes to different aspects of sound, and tons of strategies for introducing layers of nuance and control into your visuals. And while this level of mastery can seem difficult to approach, getting there is always just a matter of taking time to learn, and to discover new worlds through playful experimentation.

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The 3 best AI synthetic video generators you can use today — tested and compared

Synthetic video generators use AI to build a video from scratch, we tried them out

What Midjourney did for still images and ChatGPT did for writing, a new crop of apps is aiming to do for video — spitting out animated clips based just on a prompt. 

Unlike full AI video editing apps such as Runway Gen-1 or script-to-movie programs like InVideo, these new programs don't work off existing videos, but rather create completely synthetic movies.

This is a very nascent field, though — far behind still-image and text generators. 

Clips are limited to 4 seconds or less, depending on the service; and they often have a cartoonish or hand-sketched appearance. You won't be fooling your friends with deepfakes from these services any time soon. That said, these synthetic video creators represent an exciting frontier in AI creativity tools — one that developers and artists will be pushing forward quickly in the coming months and years.

What makes the best AI video generators? 

Realism and detail are the key factors in synthetic video. None of these apps fully mimic real-life, but they sometimes get close. Runway Gen-2, for instance, can even model shifting lighting and fine movements. Beyond tackling shapes, colors, and movement, apps have to provide enough resolution to make their creations clear. 

Most don't do that (resolutions range from 480p to 1080p). And the square format most use seems more set for meme-like online posting than an attempt at movie creation. Of course, at a maximum of 4 seconds, none of these clips approach movie proportions, but some are even shorter — at two seconds.

Sometimes, however, synthetic videos have too much detail with surrealistic hallucinations found neither in real life nor in the prompts that users feed them. 

We've seen things like cats whose bodies disappear and eating utensils that melt into people's faces. All that could be cool if it were specified in the prompt, but it wasn't. It appears that there may be a direct relationship between the sophistication of the app and its tendency to hallucinate. Programs that try to provide more detail may also provide surreal details.

Synthetic videos have too much detail with surrealistic hallucinations found neither in real life nor in the prompts that users feed them.

Whatever the app's qualities, the ability to refine results is key. For instance, how well can it understand complex prompts about colors, camera angles, movements, and more? Some apps also provide filters for different stylistic looks.

But however good the app, it may take several ties to get things right: Generative AI yields different results every time. To account for this, we gave each app three tries for each prompt we tested and then chose the best. We also set the apps to their highest quality settings (for attributes such as resolution and framerate). These online tools were all tested in the Chrome browser.

Price matters, too. While one of these apps is free, the others charge either by the length of clips produced or by the number of video-creation prompts. Pricing gets confusing quickly, with billing by week or month, discounts for year-long subscriptions, and the ability to buy supplements to subscriptions. 

Unless otherwise noted, we priced services based on the smallest possible commitment--by the month or by the week.

Synthetic AI generators compared

Product	Price	Terms	Format	Max duration (seconds)	Max resolution	File type	Accessible
Gencraft	3.99/week	25 prompts/day	square	2	720p	MP4	Web, Mobile
ModelScope	Free	N/A	square	4	720p	MP4	Web
RunwayML	$15/month (625 credits)	5 credits per second	16:9	4	720p	MP4	Web

Runway Gen-2

Reasons to buy, reasons to avoid.

Runway Gen-2 is the company's first product to create entirely synthetic AI video. (Gen-1 uses AI to modify existing videos.) As such, it shows a lot of promise, but also a lot of quirks. The app generates detailed, rich-looking clips in 16:9 widescreen fashion at 1080p resolution, with sophisticated effects. A movie we made of a cat, for instance, shows flickering of its whiskers, trees swaying in the wind, and light and shadow dancing across its face. The images aren't quite realistic, but rather sophisticated renderings — somewhere between live footage and the best videogame cut scenes.

The app can also understand complex prompts: not just "orange tabby cat," but also "orange tabby cat on a white carpet rolling a pink ball of yarn, warm lighting, realistic." Other rival apps such as Gencraft can also understand these specifications, but they show less detail and sophistication in their renderings.

Perhaps as a side effect of how ambitious Runway Gen-2 is, it can also go too far in its creativity. The app had a tendency to wild hallucinations that we didn't see in rivals. In one attempt at rendering the tabby, for instance, the cat was absent, but the ball of yarn sprouted in human hand. In another test, a horse gained a bicycle wheel. Generative AI is a trial-and-error game, though, and we generally got pretty good results within three tries. Although sometimes even the best attempts had mild hallucinations--like an otherworldly glowing white orb that floated between the cat and its ball of yarn.

Each of these goofs cost you — although not too much. Gen-2's pricing is based on credits: Five of them buys a single second of video. New users get 125 free credits, before having to upgrade to monthly plans, starting at $15 for 625 credits that expire at the end of the month. You can purchase additional, non-expiring credits starting at $10 for 1000.

Gencraft is one of the pioneers in the emerging field of pure text-to-video generators. But as such, it has limits. The app was adept at rendering an orange Tabby cat, for instance, and it could understand sophisticated modifiers in the prompt, such as adding a white carpet and pink ball of yarn (and it's relatively fast--rendering its two-second videos in under 30 seconds.) But getting cats right is pretty much a prerequisite for any online creation tool. Gencraft struggled with newer internet memes, such as "Astronaut riding a horse" or "Will Smith eating spaghetti." These images were blocky or distorted: Smith's head seemed to change shape, and his eyes bulged, for instance.

Gencraft does provide some tools to fine-tune your results. You can choose from a collection of 31 styles. Our Tabby appeared radically different in the Cartoon, Oil Painting, Abstract, and Realistic options. ("Realistic" is a bit superfluous, since images tend towards that if you simply opt to not pick a style at all.) You can also specify keywords of topics or attributes you'd like to exclude from a video, such as "cross-eyed" or "blurry background."

Gencraft does have some additional limitations. It's capped at two-second videos that appear in a square format. Rival Runway Gen-2 and the free ModelScope support up to four seconds; and Runway also provides a 16:9 wide format and 1080p resolution (vs. 720p for Gencraft).

Gencraft offers a free tier that allows you 10 video-creation prompts per day, but the clips have watermarks. You can upgrade to 25 watermark-free prompts per day for $3.99 a week, or unlimited prompts for $9.99 a week.

ModelScope Text to Video Synthesis became an internet sensation when it was used to generate the "will smith eating spaghetti" video meme. And you have plenty opportunity to try out your own crazy ideas. Hosted on AI developer hub Hugging Face, ModelScope is free of charge and can produce videos of up to 4 seconds, which is long for this emerging tech.

Of course "free" has a downside: You're at the whim of the site's servers, which often get overwhelmed and can't complete your job on the first (or second, or third) try. Pushing the tool to its highest quality settings (such as framerate) further hurts your chances. Sometimes, you just have to wait for a quieter time to get things done. And Hugging Face doesn't provide storage: You'll have to download your videos right after making them. (Don't wait, or a server glitch might wipe them out.)

ModelScope's clips don't have the high-end detailing of Runway Gen-2's creations (although they also don't have Runway's hallucinatory images). But they have a high-quality cartoony quality rivaling paid providers Gencraft and Vercel. Its rendering of an orange Tabby cat was attractive. Although like all ModelScope subjects, the cat was quite fidgety, jumping all around. Will Smith was also quite frenetic, stuffing pasta into his mouth with bare hands. But, cartoony as it was, the subject was quite recognizable as the famous actor. Gencraft's also-fidgety version was more distorted. Runway Gen-2 created a dreamy, surreal version that looked cool but didn't look much like Smith.

With support from the developer community and a price tag of zero, ModelScope is worth keeping up with as this tech rapidly advances.

Want to know more about using AI for creative work? Here's our breakdown of the 5 best text-to-video AI image generators.

More from Tom's Guide

• Runway Gen-2 supercharges AI video generation — and I tried it
• Meta's MusicGen AI makes music clips from text — here's how to try it
• Google announces big AI upgrades — including identifying skin conditions

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Sean Captain is a freelance technology and science writer, editor and photographer. At Tom's Guide, he has reviewed cameras, including most of Sony's Alpha A6000-series mirrorless cameras, as well as other photography-related content. He has also written for Fast Company, The New York Times, The Wall Street Journal, and Wired. 

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The Basics Of Analog Video Synthesis

In the latest Perfect Circuit video, LA-based artist Alex Pelly demonstrates the basics of analog video synthesis , from thoughts about planning a modular video synth system to the functions of individual modules.

Pelly discusses modules by LZX Industries and Erogenous Tones in depth, showing how they can be used as the core of a video-oriented modular system. And by providing an extended tour of her personal system and a detailed patch walkthrough, she demonstrates some tactics for harnessing feedback, audio reactivity, and hands-on control to create video that dynamically evolves along with music.

11 thoughts on “ The Basics Of Analog Video Synthesis ”

Learned a lot of valuable knowledge. Thank you for sharing your impressive expertise.

I am probably 5 large in with about 1/4 of the modules Alex has, and it’s fun to make things happen, but I’m still clueless. She should consider doing private Zoom lessons or something like that since I think I could really benefit more from an hour one-on-one learning about my specific modules.

I’d be happy to do some zoom lessons about video synthesis! check out the stuff I’m doing and get in touch https://www.instagram.com/nicholepichon/ here

Hi, very interesting ! I was wondering if there were resources on “DIGITAL video synthesis” as I am more interested by the precise aspect of digital vs. the sloppy side of analog… 😉

MUST RESIST. I do not need *another* crazy analog electronics hobby. That said, this is a great video and I sincerely hope that a few of my friends dive into analog video synthesis and show me their amazing rigs.

Anyone kow the artist behind the music . Lovely stuff! Who is it?

Careful, I gave a compliment as well, and triggered somebody, they ended up deleting my response. They’ll probably delete this, just for bringing it up.

Looked at some of these modules awhile back. I wasn’t too impressed. I was expecting Scanimate type animations and it didn’t appear to deliver. I’m not going back to find out, but I think it had something to do with how the scanimate system could deflect the o-scope crt beam and break it up line by line. Dunno if modern systems can do the same. Didn’t have the ability last time I checked, though you could build a setup yourself but fast analog multipliers are expensive. ymmv.

https://scanlines.xyz/t/raspberry-pi-based-video-gear/99 check out spectral mesh it is just a raspberry pi based openframeworks setup very easy to get started with and fairly cheap to make yourself. These are just totally different areas of video synthesis. She is doing standard shape synthesis and you are talking about sending video to a oscilliscope and rescanning it. What you are looking for has been perfectly doable with a visual cortex for a long time now you just need a scope to accompany it.

you just have to know how to get what you want.

looks very retro :/

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AI for Video Synthesis

How do we use AI to generate new videos from scratch?

In my last article in this series on The AI Developer’s Toolkit , I introduced you to the three most popular AI tools for video analysis . These tools allowed us to extract useful information from digital video.

However, there are many cases where want to generate new videos from scratch. This set of tasks is referred to as video synthesis (aka. “deep fakes”).

In this article, I’ll introduce you to the three most popular AI tools for video synthesis.

Video Interpolation

Video interpolation allows us to predict missing video frames given both previous and subsequent frames. It answers the question: “what content should go in this missing video frame?”

For example, we can create slow-motion footage from existing regular-speed footage. We provide the video-interpolation model with a low-frame-rate video as input. Then the model produces a high-frame-rate video as output.

Video interpolation is useful for a variety of video-editing tasks. For example:

• creating super-slow-motion videos using frame-rate-conversion
• restoring old film strips with inconsistent frame rates
• smoothing out security footage recorded using a video multiplexer

Video Prediction

Video prediction allows us to predictively synthesize future video frames based on a few preceding video frames. It answers the question, “what will likely happen next in this video?”

For example, given 10 frames of a golf video, we can predict the next 30 frames . We provide the video-prediction model with a few frames of video as input. Then the model produces a prediction of the next few frames of the video as output.

Video prediction is currently an active area of research, so there aren’t many practical applications yet. However, as you can imagine, this tool will likely be quite useful for a wide variety of video-generation tasks in the near future.

Video transfer

Video transfer (aka. video-to-video synthesis) allows us to synthesize entirely new videos from a more simplified input video. Essentially, it allows us to create entirely new videos from scratch.

For example, we can use a semantically-segmented video to produce a completely new video from scratch. We provide the video-transfer model with a video containing semantically-labeled pixels as input. Then the model produces a realistic video that represents the labels as output.

Video transfer is also an active area of research so there currently aren’t many practical applications. However, once again, you can imagine what we might be doing with this technology in the very near future.

Other Tools

Beyond the three examples that we’ve seen so far, there are also a variety of other AI tools for video synthesis. For example:

• Video completion – which is like image completion but for motion video
• Video face synthesis – which allows us to create synthetic videos of people speaking
• Video pose transfer – which allows us to create synthetic videos of people’s movements
• Video lip syncing – which allows us to apply the lip movements from an audio track to a video track

As we can see, video-synthesis tools allow us to transform existing videos and create new videos from scratch.

If you’d like to learn how to use all of the tools listed above, please watch my online course: The AI Developer’s Toolkit .

The future belongs who those who invest in AI today. Don’t get left behind!

[Image source: Video-to-Video Synthesis ]

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We propose a novel approach for 3D video synthesis that is able to represent multi-view video recordings of a dynamic real-world scene in a compact, yet expressive representation that enables high-quality view synthesis and motion interpolation. Our approach takes the high quality and compactness of static neural radiance fields in a new direction: to a model-free, dynamic setting. At the core of our approach is a novel time-conditioned neural radiance fields that represents scene dynamics using a set of compact latent codes. To exploit the fact that changes between adjacent frames of a video are typically small and locally consistent, we propose two novel strategies for efficient training of our neural network: 1) An efficient hierarchical training scheme, and 2) an importance sampling strategy that selects the next rays for training based on the temporal variation of the input videos. In combination, these two strategies significantly boost the training speed, lead to fast convergence of the training process, and enable high quality results. Our learned representation is highly compact and able to represent a 10 second 30 FPS multi-view video recording by 18 cameras with a model size of just 28MB. We demonstrate that our method can render high-fidelity wide-angle novel views at over 1K resolution, even for highly complex and dynamic scenes. We perform an extensive qualitative and quantitative evaluation that shows that our approach outperforms the current state of the art. Project website: .

Acknowledgements

We thank Rick Szeliski, Anton Kaplanyan, Brian Cabral, Zhao Dong, Samir Aroudj for providing feedback to this project, Daniel Andersen for helping with photorealism evaluation, Joey Conrad and Shobhit Verma for designing and building our capture rig. We thank the website template from .

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Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models

Latent Diffusion Models (LDMs) enable high-quality image synthesis while avoiding excessive compute demands by training a diffusion model in a compressed lower-dimensional latent space. Here, we apply the LDM paradigm to high-resolution video generation, a particularly resource-intensive task. We first pre-train an LDM on images only; then, we turn the image generator into a video generator by introducing a temporal dimension to the latent space diffusion model and fine-tuning on encoded image sequences, i.e., videos. Similarly, we temporally align diffusion model upsamplers, turning them into temporally consistent video super resolution models. We focus on two relevant real-world applications: Simulation of in-the-wild driving data and creative content creation with text-to-video modeling. In particular, we validate our Video LDM on real driving videos of resolution 512 x 1024, achieving state-of-the-art performance. Furthermore, our approach can easily leverage off-the-shelf pre-trained image LDMs, as we only need to train a temporal alignment model in that case. Doing so, we turn the publicly available, state-of-the-art text-to-image LDM Stable Diffusion into an efficient and expressive text-to-video model with resolution up to 1280 x 2048. We show that the temporal layers trained in this way generalize to different fine-tuned text-to-image LDMs. Utilizing this property, we show the first results for personalized text-to-video generation, opening exciting directions for future content creation.

Animation of temporal video fine-tuning in our Video Latent Diffusion Models (Video LDMs). We turn pre-trained image diffusion models into temporally consistent video generators. Initially, different samples of a batch synthesized by the model are independent. After temporal video fine-tuning, the samples are temporally aligned and form coherent videos. The stochastic generation processes before and after fine-tuning are visualised for a diffusion model of a one-dimensional toy distribution. For clarity, the figure corresponds to alignment in pixel space. In practice, we perform alignment in LDM's latent space and obtain videos after applying LDM's decoder.

Video Latent Diffusion Models

We present Video Latent Diffusion Models (Video LDMs) for computationally efficient high-resolution video generation. To alleviate the intensive compute and memory demands of high-resolution video synthesis, we leverage the LDM paradigm and extend it to video generation. Our Video LDMs map videos into a compressed latent space and model sequences of latent variables corresponding to the video frames (see animation above). We initialize the models from image LDMs and insert temporal layers into the LDMs' denoising neural networks to temporally model encoded video frame sequences. The temporal layers are based on temporal attention as well as 3D convolutions. We also fine-tune the model's decoder for video generation (see figure below).

Latent diffusion model framework and video fine-tuning of decoder. Top: During temporal decoder fine-tuning, we process video sequences with a frozen per-frame encoder and enforce temporally coherent reconstructions across frames. We additionally employ a video-aware discriminator. Bottom: in LDMs, a diffusion model is trained in latent space. It synthesizes latent features, which are then transformed through the decoder into images. Note that in practice we model entire videos and video fine-tune the latent diffusion model to generate temporally consistent frame sequences.

Our Video LDM initially generates sparse keyframes at low frame rates, which are then temporally upsampled twice by another interpolation latent diffusion model. Moreover, optionally training Video LDMs for video prediction by conditioning on starting frames allows us to generate long videos in an autoregressive manner. To achieve high-resolution generation, we further leverage spatial diffusion model upsamplers and temporally align them for video upsampling. The entire generation stack is shown below.

Video LDM Stack. We first generate sparse key frames. Then we temporally interpolate in two steps with the same interpolation model to achieve high frame rates. These operations use latent diffusion models (LDMs) that share the same image backbone. Finally, the latent video is decoded to pixel space and optionally a video upsampler diffusion model is applied.

Applications. We validate our approach on two relevant but distinct applications: Generation of in-the-wild driving scene videos and creative content creation with text-to-video modeling. For driving video synthesis, our Video LDM enables generation of temporally coherent, multiple minute long videos at resolution 512 x 1024, achieving state-of-the-art performance. For text-to-video, we demonstrate synthesis of short videos of several seconds lengths with resolution up to 1280 x 2048, leveraging Stable Diffusion as backbone image LDM as well as the Stable Diffusion upscaler. We also explore the convolutional-in-time application of our models as an alternative approach to extend the length of videos. Our main keyframe models only train the newly inserted temporal layers, but do not touch the layers of the backbone image LDM. Because of that the learnt temporal layers can be transferred to other image LDM backbones, for instance to ones that have been fine-tuned with DreamBooth. Leveraging this property, we additionally show initial results for personalized text-to-video generation.

Text-to-Video Synthesis

Many generated videos can be found at the top of the page as well as here . The generated videos have a resolution of 1280 x 2048 pixels, consist of 113 frames and are rendered at 24 fps, resulting in 4.7 second long clips. Our Video LDM for text-to-video generation is based on Stable Diffusion and has a total of 4.1B parameters, including all components except the CLIP text encoder. Only 2.7B of these parameters are trained on videos. This means that our models are significantly smaller than those of several concurrent works. Nevertheless, we can produce high-resolution, temporally consistent and diverse videos. This can be attributed to the efficient LDM approach. Below is another text-to-video sample, one of our favorites.

Text prompt: "A teddy bear is playing the electric guitar, high definition, 4k."

Personalized Video Generation. We insert the temporal layers that were trained for our Video LDM for text-to-video synthesis into image LDM backbones that we previously fine-tuned on a set of images following DreamBooth . The temporal layers generalize to the DreamBooth checkpoints, thereby enabling personalized text-to-video generation.

Training images for DreamBooth.

Text prompt: "A sks cat playing in the grass."

Text prompt: "A sks cat getting up."

Text prompt: "A sks building next to the Eiffel Tower."

Text prompt: "Waves crashing against a sks building, ominous lighting."

Text prompt: "A sks frog playing a guitar in a band."

Text prompt: "A sks frog writing a scientific research paper."

Text prompt: "A sks tea pot floating in the ocean."

Text prompt: "A sks tea pot on top of a building in New York, drone flight, 4k."

Convolutional-in-Time Synthesis. We also explored synthesizing slightly longer videos "for free" by applying our learnt temporal layers convolutionally in time. The below videos consist of 175 frames rendered at 24 fps, resulting in 7.3 second long clips. A minor degradation in quality can be observed.

Text prompt: "Teddy bear walking down 5th Avenue, front view, beautiful sunset, close up, high definition, 4k."

Text prompt: "Waves crashing against a lone lighthouse, ominous lighting."

Driving Scene Video Generation

We also train a Video LDM on in-the-wild real driving scene videos and generate videos at 512 x 1024 resolution. Here, we are additionally training prediction models to enable long video generation, allowing us to generate temporally coherent videos that are several minutes long. Below we show four short synthesized videos. Furthermore, several 5 minute long generated videos can be found here .

Specific Driving Scenario Simulation. In practice, we may be interested in simulating a specific scene. To this end, we trained a bounding box-conditioned image-only LDM. Leveraging this model, we can place bounding boxes to construct a setting of interest, synthesize a corresponding starting frame, and then generate plausible videos starting from the designed scene. Below, the image on the left hand side is the initial frame that was generated based on the shown bounding boxes. On the right hand side, a video starting from that frame is generated.

Multimodal Driving Scenario Prediction. As another potentially relevant application, we can take the same starting frame and generate multiple plausible rollouts. In the two sets of videos below, synthesis starts from the same initial frame.

Limitations

This is an NVIDIA research project, and the data sources used are for research purposes only and not intended for commercial application or use.

Andreas Blattmann*, Robin Rombach*, Huan Ling*, Tim Dockhorn*, Seung Wook Kim, Sanja Fidler, Karsten Kreis

* Equal contribution.

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023

Video Synthesis: Early Days and New Developments

Date: 8:50 - 12:30 am (IST), Octorber 24, 2022. Hall: N&G (80)

The recordings are all available under this link .

The introduction of generative adversarial networks in 2014 had a profound impact on video synthesis. Initial works generated videos with plain backgrounds and simple motions. Image synthesis advanced quite rapidly over the years. Multiple works in video synthesis capitalized on this success. Various subfields of video synthesis were introduced: prediction, animation, retargeting, manipulation, and stylization. Many of them led to a number of practical applications, democratizing video editing for non-experienced users and sparking start-ups. With the introduction of language-based models, image-based diffusion and large-scale datasets, video synthesis is seeing substantial improvement, with students, researchers andpractitioners wanting to enter and contribute to the domain. Our tutorial will help them get the necessary knowledge, understand challenges and benchmarks, and choose a promising research direction. For practitioners, our tutorial will provide a detailed overview of the domain. We expect an attendee to have intermediate knowledge of CV & ML.

Backbone architectures and frameworks that are necessary for further topics: GANs, diffusion, generative transformers, and quantized representations.
Early and recent frameworks for synthesizing frames from noise, actions, and images.
Methods for unsupervised and supervised animation. The former supports a variety of object categories, while the latter requires object-specific prior, such as a morphable face or body model and 2D or 3D keypoints.
Multimodal video synthesis: video synthesis methods conditioned on text, sketches, images, or other modalities. Interactive video synthesis: a recently emerged group of works that enable user interaction while the video is being generated.

About the speakers

Sergey Tulyakov is a Principal Research Scientist at Snap Inc, where he leads the Creative Vision team. His work focuses on creating methods for manipulating the world via computer vision and machine learning. This includes human and object understanding, photorealistic manipulation and animation, video synthesis, prediction and retargeting. He pioneered the unsupervised image animation domain with MonkeyNet and First Order Motion Model that sparked a number of startups in the domain. His work on Interactive Video Stylization received the Best in Show Award at SIGGRAPH Real-Time Live! 2020. He has published 30+ top conference papers, journals and patents resulting in multiple innovative products, including Snapchat Pet Tracking, OurBaby, Real-time Neural Lenses (gender swap, baby face, aging lens, face animation) and many others. Before joining Snap Inc., Sergey was with Carnegie Mellon University, Microsoft, NVIDIA. He holds a PhD degree from the University of Trento, Italy.

Jian Ren is a Research Scientist in the Creative Vision team at Snap Research. He got Ph.D. in Computer Engineering from Rutgers University in 2019. He is interested in image and video generation and manipulation, and efficient neural networks. Before joining Snap Inc, Jian did internships in Adobe, Snap, and Bytedance.

Stéphane Lathuilière is an associate professor (maître de conférence) at Telecom Paris, France, in the multimedia team. Until October 2019, he was a post-doctoral fellow at the University of Trento in the Multimedia and Human Understanding Group, led by Prof. Nicu Sebe and Prof. Elisa Ricci. He received the M.Sc. degree in applied mathematics and computer science from ENSIMAG, Grenoble Institute of Technology (Grenoble INP), France, in 2014. He completed his master thesis at the International Research Institute MICA (Hanoi, Vietnam). He worked towards his Ph.D. in mathematics and computer science in the Perception Team at Inria under the supervision of Dr. Radu Horaud, and obtained it from Université Grenoble Alpes (France) in 2018. His research interests cover machine learning for computer vision problems (eg. domain adaptation, continual learning) and deep models for image and video generation. He published papers in the most prestigious computer vision conferences (CVPR, ICCV, ECCV, NeurIPS) and top journals (T-PAMI).

Aliaksandr Siarohin is a Research Scientist working at Snap Research in the Creative vision team. Previously, he was a Ph.D Student at the University of Trento where he worked under the supervision of Nicu Sebe at the Multimedia and Human Understanding Group (MHUG). His research interests include machine learning for image animation, video generation, generative adversarial networks and domain adaptation. His works have been published in top computer vision and machine learning conferences. He also did internships at Snap Inc. and Google. He was a Snap Research Fellow of 2020.

Please contact Sergey Tulyakov ([email protected]) if you have question. The webpage template is by the courtesy of awesome Georgia .

Last updated: Jan. 6, 2017

• Information

Video Synthesis On Your Mac

Analog-Style Software Video Synthesizer

Lumen is a Mac App that makes it easy for you to create engaging visuals in real time. Use the same process with Lumen as you would with a hardware video synth, but with modern features only software can provide. With a semi-modular design that is both playable and deep, Lumen is the perfect way to get into video synthesis.

macOS 10.14 or later

$ 129 excl. VAT

Analog style. Digital power.

Made for synthesists

Lumen expands on the techniques of the past to bring you an entirely new process for manipulating video. You won't be in the dark, though - our comprehensive Lumen Guides and a preset library of 150+ patches will get you up-to-speed in no time.

Patchable back panel

Lumen is semi-modular, which gives you the power to reroute the synth’s signal flow. Every component is broken out, so you can create custom video effects and mind-bending feedback patches.

Plays well with others

Lumen features Syphon input & output, for interacting with other video apps. Webcam input and full-screen output let you create external feedback loops. All synthesizer knobs and buttons can be mapped to your MIDI controller.

Stay In The Loop

Subscribe to the Paracosm® News Bulletin to be the first to know about our newest products, features, and tutorials.

Instagram — Facebook — Twitter — Tumblr — Press — Contact

Many thanks to Emoji CSS

• Requirements

Cathodemer is a pixel -based video synthesizer and CRT display simulator software for VJ's, video artists and animators. More…

15.01.2022 Major Update (2.7)

14.04.2020 FAQ Updated

26.10.2019 Major 64bit Update (2.5)

15.01.2018 Update (1.7) to add NDI output, frame snatching, midi learn to ui buttons.

31.12.2017 FAQ section added to this site.

16.11.2017 Update (1.6) to improve video file playback and more.

06.10.2017 Update (1.5) to add custom resolutions, aspect ratios with vintage device presets, CC triggers for part/slot selectors, fixes to file source playback.

22.09.2017 Update (1.4) to add new OpenGL settings (rotation and zoom), custom FOURCC codec input for render, new distortion modes, MIDI note part/slot selectors, MIDI CC to Extras, general settings, GUI settings (UI color customization) and synthesizer master volume.

12.09.2017 Update (1.3) to fix Mac file browsing and figure frame Reset.

11.09.2017 Update (1.2) to fix Windows rendering and full screen aspect ratio.

08.09.2017 Released on Steam!

Cathodemer is a realtime video production instrument with a CRT display simulator. It features analog-style RGB signal oscillation synthesis, input source mixing and manipulation, recursive color shape synthesis, sprite animation engine and various classic video effects. All parameters can be controlled via MIDI and audio input. Video output can be projected to video projector, rendered to file or routed to other video applications via framesharing.

Copyright (c) 2022 Joel Kivelä. All rights reserved.

World-Consistent Video-to-Video Synthesis

Arun mallya*, ting-chun wang*, karan sapra, ming-yu liu, published at the european conference on computer vision, 2020.

We present a GAN-based approach to generate 2D world renderings that are consistent over time and viewpoints, which was not possible with prior approaches. Our method colors the 3D point cloud of the world as the camera moves through the world, coloring new regions in a manner consistent with the already colored world. It learns to render images based on the 2D projections of the point cloud to the camera in a semantically consistent manner while robustly dealing with incorrect and incomplete point clouds. Our proposed approach further shortens the gap between classical graphics rendering and neural rendering.

Summary Video

Presentation Video

Previous video

What is the task of vid2vid?

Issues with prior work.

• As highlighted at the end of the video, compared to the first frame, a lot of features are different in the last frame, even though we return back to the same viewpoint. In other words, the video is not world-consistent over long timeframes.
• We observe flickering in the outputs, indicating a lack of fine short-term consistency and realism in features such as the trees, the road markings, etc.

Achieving consistency across views and time

We believe that in order to produce realistic outputs that are consistent over time and viewpoint change, the method must be aware of the 3D structure of the world. To achieve this, we introduce the concept of guidance images , which are physically-grounded estimates of what the next output frame should look like, based on how the world has been generated so far. As alluded to in their name, the role of these guidance images is to guide the generative model to produce colors and textures that respect previous outputs.

While prior works use optical flow to warp prior outputs, our guidance image differs from this in two aspects. First, instead of using optical flow, the guidance image should be generated by using the motion field, or scene flow, which describes the true motion of each 3D point in the world. Second, the guidance image should aggregate information from all past viewpoints (and thus frames), instead of only the direct previous frames as in vid2vid. This makes sure that the generated frame is consistent with the entire history.

The figure below shows one method to generate guidance images by using point clouds and camera locations obtained by performing Structure from Motion (SfM) on an input video. In case of a game rendering engine, the ground truth scene flow can be obtained and used to generate guidance images.

Generating 3D-aware guidance images

A camera(s) with known parameters and positions travels over time \( t = 0,\cdots,N \). At \( t = 0 \), the scene is textureless and an output image is generated for this viewpoint. The output image is then back-projected to the scene and a guidance image for a subsequent camera position is generated by projecting the partially textured point cloud. Using this guidance image , the generative method can produce an output that is consistent across views and smooth over time. The guidance image can be noisy, misaligned, and have holes, and the generation method should be robust to such inputs.

As input to our model, we provide the guidance images along with the input labels such as semantic segmentation and depth maps. The inputs and generated outputs are visualized in the below video. As new 3D points become visible to the camera, colors are assigned to them by our image generator and the point cloud is updated. Note that the guidance images have holes and incorrect projections due to noisy point cloud information. Our method is robust to such noise and produces meaningful outputs.

Here, we show how the application of our method solves the issues with temporal consistency observed above with prior work. As the viewer returns back to the starting position, the produced output is very similar to that of the first image, respecting previously produced textures. The output images and transitions over time also look more realistic.

The complete network architecture

Here, we visualize our generator architecture and all its components. The main component in our generator is the novel Multi-SPADE block, which is composed of multiple SPADE layers. Each SPADE layer takes in a spatial conditioning map such as the semantic segmentation, or the optical flow-warped previous output, or the guidance image, and applies appropriate transformations on the intermediate feature maps so that the finally generated output respects the required constraints and looks realistic in the spatial and temporal domain.

The overall architecture based on the Multi-SPADE module

Each Multi-SPADE module takes input label features, warped previous frame features, and guidance images to modulate the features in each layer of our generator. The labels decide the semantic content of the output frames , the optical-flow warped previous frames ensure short-term consistency , and the guidance images ensure long-term consistency .

Sample Video Generation Results

Consistent multiview generation.

• Video-to-video synthesis is a powerful tool for converting semantic inputs to photorealistic videos.
• Existing vid2vid methods are unable to maintain long-term consistency (such as during loop closure) due to lack of the 3D structure of the world.
• We provide information about the 3D structure of the world using guidance images - projecting point clouds of the world colored so far (which can be noisy and incomplete) to the of the current camera view.
• We introduce a new architecture based on the Multi-SPADE module, which uses semantic labels, optical-flow warping, and guidance images as conditioning inputs.
• Our models improve upon the realism, short-term, and long-term consistency of generated videos.

Best Video Synthesizers in 2024: 7 Top Picks For Creating Stunning Visual Effects

We’ve all witnessed the mesmerizing dance of colors and patterns at concerts and art installations, but have you ever wondered how those hypnotic visuals come to life?

It’s the magic of video synthesizers, the unsung heroes behind the curtain.

In our quest for the most stunning visual effects, we’ve scoured the market to find the best video synthesizers out there.

Whether you’re a VJ, an artist, or just someone who loves to dabble in visual alchemy, we’ve got the lowdown on the top tools that’ll take your visuals to the next level.

Best Video Synthesizers For Stunning Visual Effects

Let’s take a look at some of the top video synthesizers for stunning visual effects.

1. LZX Industries Vidiot

Delving into the realm of analog video processing, LZX Industries Vidiot stands out for its versatility and user-friendly design.

As a compact standalone unit, it offers a comprehensive set of features that cater to the imaginative visual artist.

The Vidiot is known for its intuitive interface , which allows users to generate complex patterns and textures without a steep learning curve.

It’s been a game-changer for live performances where quick adjustments are critical.

Its colorization capabilities are particularly noteworthy, providing a broad spectrum of vibrant hues to enhance visual presentations.

The Vidiot excels in translating audio signals into engaging visual experiences in a seamless fashion.

Key Features Include –

• Robust analog signal processing,
• Direct audio-to-visual translation.

Geared towards both beginners and seasoned VJs, the Vidiot serves as an excellent tool for those who wish to explore the possibilities of analog video synthesis.

Its compact size also makes it an ideal companion for artists on the move.

Durability is another strong suit of the Vidiot, crafted to withstand the rigors of touring.

Its solid construction ensures that it holds up, whether it’s being used in a quiet studio or a dynamic live environment.

Artists have found the Vidiot to be effective not only in concerts but also in gallery settings where the subtlety and sophistication of its outputs greatly enrich the artwork’s narrative.

Through the Vidiot, LZX Industries has significantly lowered the barriers to entering the world of video synthesis.

It’s an invaluable addition to the toolkit of any visual artist looking to push boundaries and captivate audiences with stunning visual narratives.

2. Critter & Guitari ETC Video Synthesizer

When talking about innovative tools in the video synthesis domain, Critter & Guitari’s ETC stands out for its distinctive approach to creating visual effects.

Unlike traditional video synthesizers, the ETC uses Python scripts – known as modes – to generate visuals, giving users vast possibilities for customization.

Our experience with the ETC has taught us that one of the most compelling features is its real-time audio to video capabilities.

This unique trait means that musicians and VJs can directly translate the audio input into evocative visual displays during live performances.

The simplistic design of the ETC is deceptive, concealing its deep functionality.

With just a few knobs and switches, users can navigate through modes and tweak parameters, offering a highly intuitive experience that is as engaging as it is straightforward.

Among the ETC’s noteworthy characteristics are:

• Real-time audio-reactive video synthesis,
• Easily programmable with new modes through open-source Python code,
• HDMI and composite video outputs for high compatibility.

We’ve found that the versatility of output options significantly augments the utility of the ETC.

Being able to switch between HDMI and composite ensures artists are not limited by their current setup and can easily integrate the ETC into various environments.

Artists have consistently told us about their appreciation for the accessible nature of adding or editing Python scripts in the ETC.

This flexibility allows for tailored visual experiences that are limited only by one’s imagination and coding prowess.

With the increasing interest in coding within the creative community, the ETC offers a unique bridge between the realms of visual art and programming.

This synergy opens up new horizons for dynamic, algorithm-driven art installations and live performances that resonate with a tech-savvy audience.

3. Tachyons+ Psychenizer

Diving deeper into the world of video synthesis, we encounter the Tachyons+ Psychenizer.

This unit captivates with its psychedelic flair and hands-on approach to video manipulation.

As tactile analog hardware, the Psychenizer invites artists to twist and transform video signals with immediacy.

Its interface feels like an instrument unto itself, crafted to bend imagery with each turn of a knob or flick of a switch.

Known for delivering vibrant colors and dream-like visuals, the Tachyons+ Psychenizer weaves a tapestry of effects that can be as subtle or as wild as the user desires.

It achieves this through an array of built-in modifications.

Each feature unlocks new potential for visual exploration, letting creativity flow unrestricted.

When we consider the Psychenizer’s attributes, several stand out:

• Real-time color processing – altering hues and saturation on the fly,
• Texture modulation – infusing scenes with organic or otherworldly patterns,
• Signal degradation – creating lo-fi, retro aesthetics akin to VHS-era visuals.

With the Psychenizer, the output looks mesmerizing, whether projected in a dark club or used in a carefully orchestrated music video.

The flexibility to interface with other gear via CV and audio inputs expands the possibilities even further.

Musicians and visual artists find in the Psychenizer not just a tool, but a collaborator that reacts and evolves with their performance.

Our experience with the Tachyons+ Psychenizer highlights its suitability for artists who strive for an analog feel in an increasingly digital landscape.

Its uniqueness lies in the sensory experience it offers, allowing for a hands-on connection between artists and their visual medium.

Embodying the idea that beauty often lies in imperfection, its unpredictable nature is precisely what makes it such a beloved piece of equipment in the video synthesis community.

4. Bleep Labs Hss3jb

Venturing further into the realm of video synthesis, we encounter the Bleep Labs HSS3jb – a device that’s as unconventional as its name.

Known for its compact design, the HSS3jb is a powerhouse of glitchy goodness.

It’s a prime choice for artists who revel in the beauty of visual aberration and unpredictability.

Our exploration of video synthesizers wouldn’t be complete without a deep jump into this unique gadget.

The core of the HSS3jb lies in its ability to mangle video signals in creative ways that other synthesizers might shy away from.

It generates textures and patterns that can be described as mesmerizingly chaotic.

Think of it less as a tool and more as a video synthesis partner that challenges your artistic tendencies.

The HSS3jb doesn’t just modify your visuals; it invites you to reconsider what visual art could be.

With the Bleep Labs HSS3jb, artists can tap into a fascinating array of features – – Real-time control over video distortion

• Audio input for sound-reactive effects,
• Multiple modes that produce diverse and engaging visuals.

Given these compelling features, it’s clear that the HSS3jb stands out for its potential to create unique live performances.

Its ability to react to audio signals also opens up avenues for musicians and DJs to add a visual element that’s tightly integrated with their sound.

This fusion of audio and visuals is crucial in crafting immersive experiences for audiences.

Our journey through different video synthesizers such as the Tachyons+ Psychenizer and now the Bleep Labs HSS3jb shows the breadth of artistic expression that can be achieved.

It underscores the endless possibilities that these tools provide.

We’re always on the lookout for how innovations like the HSS3jb can redefine the boundaries of visual artistry.

Enthusiasts of a glitch aesthetic will find this quirky device to be an essential part of their creative arsenal.

5. Sleepy Circuits Hypno

In our quest to identify the best video synthesizers for visual effects, we’ve come across the Sleepy Circuits Hypno.

Compact and versatile , this device seamlessly marries digital and analog techniques to inspire creators.

The Hypno offers intuitive controls and an approachable interface, making real-time video synthesis accessible for both beginners and professionals.

High-quality visual effects are at your fingertips with its array of onboard features.

With dual video inputs and a multitude of modulations options, the possibilities with the Hypno are truly expansive.

It integrates easily with modular synthesizer systems and other video gear.

Key features of the Hypno include:

• Digital oscillators with a wide range of patterns and textures,
• Analog-style shape and color controls for a warm, organic feel,
• Comprehensive modulations for dynamic, reactive visuals.

6. Erogenous Tones Structure

While diving deeper into the world of video synthesizers, it’s impossible not to mention Erogenous Tones Structure .

This modular beast is a godsend for visual artists craving complexity and depth in their work.

Our exploration reveals its standout feature – patch programmability .

It offers a level of customization that unleashes endless creative possibilities for live performances and studio sessions alike.

Here’s what sets Erogenous Tones Structure apart:

• Real-time manipulation with extensive control voltage (CV) I/O ports,
• High-definition output ensuring crisp and vibrant visuals,
• Massive pattern generation capabilities with nuanced modulation.

As for integration, Erogenous Tones Structure excels in connectivity.

Its compatibility with Eurorack systems allows for seamless addition to existing audio setups, maximizing the synergy between sound and visuals.

We’ve found that the unit’s strength lies in its ability to produce intricate patterns and textures.

These patterns are not only hypnotizing but also ever-changing due to the dynamic modulation options .

The incorporation of various filter types further enhances the visual output.

Users can expect:

• Smooth transitions and blends between patterns,
• Sharpened edges for more defined shapes.

Don’t let the complexity intimidate you.

Even though it’s a marvel for those seasoned in synthesis, newcomers aren’t left behind.

With some dedication, they too can master the Erogenous Tones Structure , adding a professional flair to their visual narratives.

It’s evident that versatility is key with this device.

It fits right into any visual artist’s toolkit, whether they’re crafting backdrops for concerts or producing abstract art installations.

The integration possibilities and modulation options make it a powerful choice for pushing the boundaries of video synthesis.

7. Edirol V-4EX Four-channel Digital Video Mixer

Stepping into the world of digital video mixers, we’re impressed by the Edirol V-4EX’s capabilities.

This unit serves as an all-in-one device that artfully blends audio and video sources.

Its intuitive layout simplifies the complexity of video mixing, making it accessible for users at different proficiency levels.

The V-4EX shines with its touch monitor interface, providing direct control and preview.

Its standout features make it a must-consider for VJs and visual artists:

• Built-in touchscreen for easy operation and preview,
• Support for up to 1080p video input and output,
• HDMI inputs and outputs alongside traditional SD composite.

The V-4EX isn’t just a mixer; it’s a powerful performance tool.

With effects and transitions at your fingertips, creating dynamic presentations is effortlessly engaging.

It caters to the live performance environment with features designed to support on-the-fly adjustments and improvisation.

Compatibility with numerous video formats streamlines the workflow.

The Edirol V-4EX eliminates the need for external converters, accommodating various types of media.

It enables visual artists to incorporate a diverse range of content seamlessly.

We’re particularly impressed by the ease of integration:

• MIDI support for syncing with musical equipment and other gear,
• USB streaming for online performances and video recording.

Efficient and versatile, the V-4EX is a solid choice for enhancing live shows and events.

Its all-in-one design fosters creativity and allows artists to focus on delivering an immersive experience.

With this digital video mixer at the heart of your setup, technical limitations become a thing of the past.

8. Roland V-1HD Portable Video Switcher

The Roland V-1HD stands out as a compact and reliable solution geared towards videographers and live event producers.

This portable switcher is the perfect companion when mobility is key, allowing for the production of dynamic video content anywhere.

Ease of use is a trademark feature of the V-1HD.

With its intuitive interface, even those new to video switching can navigate its features without a hitch.

Here’s what sets the V-1HD apart:

• Lightweight and portable design – easy to carry and set up,
• Support for up to four HDMI inputs – allowing for multiple video sources,
• Full 1080p HD resolution – ensures crisp and clear video output.

Professionals will appreciate the V-1HD’s attention to high-quality video standards.

Our experiences with the device confirm its ability to deliver sharp, high-definition visuals for a range of multimedia projects.

Also, the V-1HD offers a suite of creative transition effects to enhance live video productions.

From mix to flip, wipe to squeeze, these effects can take a live performance to the next level.

Incorporating audio mixing capabilities, the V-1HD becomes even more versatile.

It handles both video and audio with finesse, simplifying workflow and reducing the need for additional equipment.

For those concerned with live video broadcasts, the Roland V-1HD doesn’t disappoint.

It delivers seamless performance that keeps audiences engaged with professional-grade video switching that’s surprisingly straightforward.

9. BPMC Premium Cable

In the vibrant world of video synthesis, connectivity is king.

That’s where the BPMC Premium Cable makes its grand entrance – ensuring that the complex web of devices communicates flawlessly.

These cables are engineered specifically for the high demands of video synthesizers.

They promise unparalleled signal fidelity , a must-have for anyone serious about their visual output.

It’s not just about the connection quality, though.

BPMC Premium Cables also boast a robust build that stands up to the wear and tear of live performance.

With heavy-duty connectors and flexible cable shielding , they’re crafted to last.

In terms of compatibility, these cables are a universal key.

They effortlessly plug into a wide array of devices – from vintage synthesizers to cutting-edge switchers like the Roland V-1HD.

Here’s what sets them apart:

• Optimized for video – preserves signal quality across various formats,
• Durable construction – ready to handle the rigors of the road,
• Universal compatibility – connects with a diverse range of video synthesis gear.

With BPMC Premium Cables, performance hiccups due to poor connections are virtually eliminated.

These cables are the silent heroes that let videographers and live event producers focus on what matters – creating mesmerizing visuals.

Reliability and quality define our experience with BPMC cables.

We’ve noticed fewer technical disruptions, contributing to an overall smoother workflow.

Harnessing the best in connectivity technology, BPMC Premium Cables are the unsung backbone of any serious video synthesis setup.

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Exploring The Art Of Visual Effects With Video Synthesizers

For creatives diving into the world of visual effects, video synthesizers are the paintbrushes of the digital age.

The tools they provide unlock a spectrum of possibilities – from subtle background enhancements to immersive, mind-bending visuals.

The beauty of these devices lies in their ability to bend reality to our creative wills.

They can distort images, manipulate colors, and synchronize with music to create a holistic sensory experience.

Here’s a snapshot of the aspects that video synthesizers touch upon:

• Real-time manipulation of video signals,
• Integration with audio for synchronized effects,
• Layering and texturing for depth enhancement.

As we jump deeper into specific models, certain features stand out for their ingenuity.

Take for instance the Critter & Guitari ETC Video Synthesizer – it reacts to music in real-time, turning audio input into captivating visual output.

It’s simplistic in design yet powerful in performance.

Adding to our toolkit, the Edirol V-8 Eight Channel Video Mixer offers remarkable control.

With various transitions and effects at our fingertips, we can orchestrate complex visual narratives.

Its features include:

• Seamless switching between video sources,
• Numerous built-in video effects for instant application.

With these instruments in our arsenal, the art of visual effects is limited only by our imagination.

We’ve seen in the previous section how the O’Tool Plus allows us to analyze and adjust our signals with precision.

It’s essential for ensuring our creations are not just compelling, but clear and professional-looking too.

Top Video Synthesizers – Wrap Up

We’ve explored some of the most innovative video synthesizers that can elevate your visual effects to new heights.

Whether you’re reacting to music in real-time with the Critter & Guitari ETC or seamlessly switching between channels with the Edirol V-8, these tools are game-changers.

Remember, the right video synthesizer doesn’t just add effects—it transforms your visuals into a synchronized audiovisual experience.

With devices like the Dave Jones Design O’Tool Plus, precision and professionalism are at your fingertips.

Now it’s your turn to harness these powerful tools and unleash your creativity.

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Video-to-Video Synthesis

Part of Advances in Neural Information Processing Systems 31 (NeurIPS 2018)

Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Guilin Liu, Andrew Tao, Jan Kautz, Bryan Catanzaro

We study the problem of video-to-video synthesis, whose goal is to learn a mapping function from an input source video (e.g., a sequence of semantic segmentation masks) to an output photorealistic video that precisely depicts the content of the source video. While its image counterpart, the image-to-image translation problem, is a popular topic, the video-to-video synthesis problem is less explored in the literature. Without modeling temporal dynamics, directly applying existing image synthesis approaches to an input video often results in temporally incoherent videos of low visual quality. In this paper, we propose a video-to-video synthesis approach under the generative adversarial learning framework. Through carefully-designed generators and discriminators, coupled with a spatio-temporal adversarial objective, we achieve high-resolution, photorealistic, temporally coherent video results on a diverse set of input formats including segmentation masks, sketches, and poses. Experiments on multiple benchmarks show the advantage of our method compared to strong baselines. In particular, our model is capable of synthesizing 2K resolution videos of street scenes up to 30 seconds long, which significantly advances the state-of-the-art of video synthesis. Finally, we apply our method to future video prediction, outperforming several competing systems. Code, models, and more results are available at our website: https://github.com/NVIDIA/vid2vid. (Please use Adobe Reader to see the embedded videos in the paper.)

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Computer Science > Computer Vision and Pattern Recognition

Title: neural 3d video synthesis from multi-view video.

Abstract: We propose a novel approach for 3D video synthesis that is able to represent multi-view video recordings of a dynamic real-world scene in a compact, yet expressive representation that enables high-quality view synthesis and motion interpolation. Our approach takes the high quality and compactness of static neural radiance fields in a new direction: to a model-free, dynamic setting. At the core of our approach is a novel time-conditioned neural radiance field that represents scene dynamics using a set of compact latent codes. We are able to significantly boost the training speed and perceptual quality of the generated imagery by a novel hierarchical training scheme in combination with ray importance sampling. Our learned representation is highly compact and able to represent a 10 second 30 FPS multiview video recording by 18 cameras with a model size of only 28MB. We demonstrate that our method can render high-fidelity wide-angle novel views at over 1K resolution, even for complex and dynamic scenes. We perform an extensive qualitative and quantitative evaluation that shows that our approach outperforms the state of the art. Project website: this https URL .

Comments:	Accepted as an oral presentation for CVPR 2022. Project website:
Subjects:	Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
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