Learning associations across modalities is critical for robust multimodal reasoning, especially when a modality may be missing during inference. In this paper, we study this problem in the context of audio-conditioned visual synthesis – a task that is important, for example, in occlusion reasoning. Specifically, our goal is to generate future video frames and their motion dynamics conditioned on audio and a few past frames.
Predicting the future frames of a video is a challenging task, in part due to the underlying stochastic real-world phenomena. Prior approaches to solve this task typically estimate a latent prior characterizing this stochasticity, however do not account for the predictive uncertainty of the (deep learning) model. Such approaches often derive the training signal from the mean-squared error (MSE) between the generated frame and the ground truth, which can lead to sub-optimal training, especially when the predictive uncertainty is high.
Video frames are often dropped during compression at very low bit rates. At the decoder, a missing frame interpolation method synthesizes the missed frames. We propose a two step motion estimation method for the interoplation. More specifically, the coarse motion vector field is refined at the decoder using mesh-based motion estimation instead of using computationally intensive dense motion estimation.