https://nvlabs.github.io/face-vid2vid/ One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing Ting-Chun Wang Arun Mallya Ming-Yu Liu NVIDIA Corporation CVPR 2021 (Oral) [Paper] [arXiv] [Video] [teaser] Abstract We propose a neural talking-head video synthesis model and demonstrate its application to video conferencing. Our model learns to synthesize a talking-head video using a source image containing the target person's appearance and a driving video that dictates the motion in the output. Our motion is encoded based on a novel keypoint representation, where the identity-specific and motion-related information is decomposed unsupervisedly. Extensive experimental validation shows that our model outperforms competing methods on benchmark datasets. Moreover, our compact keypoint representation enables a video conferencing system that achieves the same visual quality as the commercial H.264 standard while only using one-tenth of the bandwidth. Besides, we show our keypoint representation allows the user to rotate the head during synthesis, which is useful for simulating a face-to-face video conferencing experience. paper thumbnail Paper arXiv Citation Ting-Chun Wang, Arun Mallya, Ming-Yu Liu. "One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing." In CVPR, 2021. Bibtex Code This work is based upon Imaginaire. For more details, please visit Maxine. Demo Please visit this page for an online demo. Our Example Results Video Reconstruction Head Rotation Face Frontalization Motion Transfer Citation If you find this useful for your research, please use the following. @inproceedings{wang2021facevid2vid, title={One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing}, author={Ting-Chun Wang and Arun Mallya and Ming-Yu Liu}, booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, year={2021} } web page statistics from GoStats