Motion and Context-Aware Audio-Visual Conditioned Video Prediction

• URL: http://arxiv.org/abs/2212.04679v3
• Date: Wed, 20 Sep 2023 11:58:10 GMT
• Title: Motion and Context-Aware Audio-Visual Conditioned Video Prediction
• Authors: Yating Xu, Conghui Hu, Gim Hee Lee
• Abstract summary: We decouple the audio-visual conditioned video prediction into motion and appearance modeling. The multimodal motion estimation predicts future optical flow based on the audio-motion correlation. We propose context-aware refinement to address the diminishing of the global appearance context.
• Score: 58.9467115916639
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The existing state-of-the-art method for audio-visual conditioned video prediction uses the latent codes of the audio-visual frames from a multimodal stochastic network and a frame encoder to predict the next visual frame. However, a direct inference of per-pixel intensity for the next visual frame is extremely challenging because of the high-dimensional image space. To this end, we decouple the audio-visual conditioned video prediction into motion and appearance modeling. The multimodal motion estimation predicts future optical flow based on the audio-motion correlation. The visual branch recalls from the motion memory built from the audio features to enable better long term prediction. We further propose context-aware refinement to address the diminishing of the global appearance context in the long-term continuous warping. The global appearance context is extracted by the context encoder and manipulated by motion-conditioned affine transformation before fusion with features of warped frames. Experimental results show that our method achieves competitive results on existing benchmarks.

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