| Full text | |
| Author(s): |
Albarracin, Juan F. Hernandez
;
Ramirez Rivera, Adin
Total Authors: 2
|
| Document type: | Journal article |
| Source: | IEEE Transactions on Image Processing; v. 31, p. 10-pg., 2022-01-01. |
| Abstract | |
Independent components within low-dimensional representations are essential inputs in several downstream tasks, and provide explanations over the observed data. Video-based disentangled factors of variation provide low-dimensional representations that can be identified and used to feed task-specific models. We introduce MTC-VAE, a self-supervised motion-transfer VAE model to disentangle motion and content from videos. Unlike previous work on video content-motion disentanglement, we adopt a chunk-wise modeling approach and take advantage of the motion information contained in spatiotemporal neighborhoods. Our model yields independent per-chunk representations that preserve temporal consistency. Hence, we reconstruct whole videos in a single forward-pass. We extend the ELBO's log-likelihood term and include a Blind Reenactment Loss as an inductive bias to leverage motion disentanglement, under the assumption that swapping motion features yields reenactment between two videos. We evaluate our model with recently-proposed disentanglement metrics and show that it outperforms a variety of methods for video motion-content disentanglement. Experiments on video reenactment show the effectiveness of our disentanglement in the input space where our model outperforms the baselines in reconstruction quality and motion alignment. (AU) | |
| FAPESP's process: | 17/16144-2 - Video-to-video dynamics transfer with deep generative models |
| Grantee: | Juan Felipe Hernández Albarracín |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 19/07257-3 - Learning representations through deep generative models on video |
| Grantee: | Gerberth Adín Ramírez Rivera |
| Support Opportunities: | Regular Research Grants |