Marcus Edel
September 14, 2022
Reading time:
Over the past few years, different video codecs have been successfully developed, including H.265 and VP9, to meet the needs of various applicationsranging from video conferencing platforms like Zoom to streaming services like YouTube and software like OBS to broadcast to different sites.
The quality of the reconstructed video using these codecs is excellent at medium-to-low bitrates, but it degrades when operating at very low bitrates. While these codecs leverage expert knowledge of human perception and carefully engineered signal processing pipelines, there has been a massive interest in replacing these handcrafted methods with machine learning approaches that learn to encode video data.
Using open source software, Collabora has developed an efficient compression pipeline that enables a face video broadcasting system that achieves the same visual quality as the H.264 standard while only using one-tenth of the bandwidth. In a nutshell, the face video compression algorithms rely on a source frame of the face, a pipeline to extract the important features from a face image, and a generator to reconstruct the face using the extracted and compressed features on the receiving side.
Animating expressive talking heads is essential for filmmaking, virtual avatars, video streaming, computer games, and mixed realities. Despite recent advances, generating realistic facial animation with little or no manual labor remains an open challenge in computer graphics. Several key factors contribute to this challenge. Traditionally the generation process needs a lot of compute, making it nontrivial to run it in real-time in a video conference setting. Facial dynamics are difficult to reconstruct using based on a few images.
We present a method that generates expressive talking-head videos from a single facial image and a driving video. The key component of our method is the prediction of the facial landmarks reflecting the facial dynamics. Based on this intermediate representation, our method works with many portrait images in a single unified framework and generalizes well for faces that were not observed during training.
A neural network extracts and encodes the locations of key facial features of the user for each frame, which is much more efficient than compressing pixel and color data. The encoded data is then passed on to a generative adversarial network along with a reference video frame captured at the beginning of the session. The GAN is trained to reconstruct the new image by projecting the facial features onto the reference frame.
We base our generator network on the image-to-image translation architecture proposed by Johnson et al., but replace downsampling and upsampling layers with residual blocks similarly. For the discriminator, we use a similar network, which consists of residual downsampling blocks without normalization layers. We also use self-attention blocks, which are inserted at 32×32 spatial resolution in all downsampling parts of the networks and at 64×64 resolution in the upsampling part of the generator.
We also integrated our Super-Resolution model on top of the reconstructed output to enhance the overall image quality without increasing the necessary bandwidth.
The video shows the video compression model in action; the first video is the H.264 compression, and the second is the reconstructed video based on a single source image and predicted landmarks for the driving video. The last video applies Super-Resolution on top of it to improve the overall video quality.
The compression pipeline can be used as a standalone tool, but it can also be embedded directly into existing video conferencing tools. Thanks to that, the model can tap into all the metadata you have about your video stream and dynamically adjust the number of landmarks to improve facial reconstruction.
Currently, the key limitation of our method is that using landmarks from a different person leads to a noticeable mismatch. In addition, our reconstruction network takes a lot of compute, hindering wider adoption for resource-constrained devices.
Our work could not have been possible without the help of countless open source resources. We hope our contributions will help others in the video compression and web conferencing community build the next generation of innovative technology. We released the code to reproduce the results.
If you have questions or ideas on how to compress your data, join us on our Gitter #lounge channel or leave a comment in the comment section.
15/08/2024
After rigorous debugging, a new unit testing framework was added to the backend compiler for NVK. This is a walkthrough of the steps taken…
01/08/2024
We're reflecting on the steps taken as we continually seek to improve Linux kernel integration. This will include more detail about the…
27/06/2024
With each board running a mainline-first Linux software stack and tested in a CI loop with the LAVA test framework, the Farm showcased Collabora's…
26/06/2024
WirePlumber 0.5 arrived recently with many new and essential features including the Smart Filter Policy, enabling audio filters to automatically…
12/06/2024
Part 3 of the cmtp-responder series with a focus on USB gadgets explores several new elements including a unified build environment with…
06/06/2024
The final installment of a series explaining how Collabora is helping shape the video virtualization story for Chromebooks with a focus…
Comments (6)
Guillaume:
Oct 04, 2022 at 08:03 AM
Hi Marcus! :)
Great work! I was wondering how could such codec be integrated with a videoconferencing web client? Would it be possible to ship it as a browser plugin or something?
Reply to this comment
Reply to this comment
Marcus Edel:
Dec 23, 2022 at 06:25 PM
Depending on the videoconferencing platform, a plugin could work. The main issue is intercepting the video stream before it gets sent out. One solution we looked into is to provide a special chrome or firefox version with the necessary fixes to make it work.
Reply to this comment
Reply to this comment
LinuxLover:
Dec 21, 2022 at 10:07 PM
Could this approach be adapted to game/desktop streaming?
Reply to this comment
Reply to this comment
Marcus Edel:
Dec 23, 2022 at 06:34 PM
This particular method focuses on web video conferencing (faces); in this setting, we can extract keypoints from the face and later use them to reconstruct the face. That said, this technique can be transferred to other areas, like arbitrary objects in a game. However, we are looking into combining foveated rendering and super-resolution, specifically targeting games, to reduce the bandwidth required.
Reply to this comment
Reply to this comment
Salvador:
Mar 14, 2023 at 05:39 PM
Amazing work sir. Regarding what you mention about h265 and obs. Would like to showcase obs with h265 or h264 encoding on rk3399, Would be that possible? If so, please co sider to give me some hints since getting vpu to work on mainline on rk3399 was always a bit difficult.
I got obs working nicely on blobs with h265 enc on rk3588, but would love to se it working decently at least up to 1080p30 on rk3399 with h264 or h265 enc.
Reply to this comment
Reply to this comment
Anthony:
May 29, 2024 at 07:51 AM
Hello Marcus, it seems really interesting, especially the updated version of it presented at IBC2023 (https://www.youtube.com/watch?v=9ATG6eb2uVE), it is said in this video that the code is open-source, however I was just able to find this link on my side: https://github.com/collabora/ml-compression, the code does not seem complete and do not include the audio part, would it be possible to get the link of the whole repository including weights for the model, model, inference code, etc.? Thank you in advance!
Reply to this comment
Reply to this comment
Add a Comment