CCVS: Context-aware Controllable Video Synthesis

Abstract

This presentation introduces a self-supervised learning approach to the synthesis of new video clips from old ones, with several new key elements for improved spatial resolution and realism: It conditions the synthesis process on contextual information for temporal continuity and ancillary information for fine control. The prediction model is doubly autoregressive, in the latent space of an autoencoder for forecasting, and in image space for updating contextual information, which is also used to enforce spatio-temporal consistency through a learnable optical flow module. Adversarial training of the autoencoder in the appearance and temporal domains is used to further improve the realism of its output. A quantizer inserted between the encoder and the transformer in charge of forecasting future frames in latent space (and its inverse inserted between the transformer and the decoder) adds even more flexibility by affording simple mechanisms for handling multimodal ancillary information for controlling the synthesis process (e.g., a few sample frames, an audio track, a trajectory in image space) and taking into account the intrinsically uncertain nature of the future by allowing multiple predictions. Experiments with an implementation of the proposed approach give very good qualitative and quantitative results on multiple tasks and standard benchmarks.Preprint. Under review.

NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion

“…In Tab. 1, N ÜWA significantly outperforms 2 181 VideoFlow [18] 3 131 LVT [31] 1 126±3 SAVP [20] 2 116 DVD-GAN-FP [7] 1 110 Video Transformer (S) [44] 1 106±3 TriVD-GAN-FP [23] 1 103 CCVS [25] 1 99±2 Video Transformer (L) [44] 1 94±2…”