Learning Spatial Adaptation and Temporal Coherence in Diffusion Models for Video Super-Resolution

Zhikai Chen, Fuchen Long, Zhaofan Qiu, Ting Yao, Wengang Zhou, Jiebo Luo, Tao Mei; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 9232-9241

Diffusion models are just at a tipping point for image super-resolution task. Nevertheless it is not trivial to capitalize on diffusion models for video super-resolution which necessitates not only the preservation of visual appearance from low-resolution to high-resolution videos but also the temporal consistency across video frames. In this paper we propose a novel approach pursuing Spatial Adaptation and Temporal Coherence (SATeCo) for video super-resolution. SATeCo pivots on learning spatial-temporal guidance from low-resolution videos to calibrate both latent-space high-resolution video denoising and pixel-space video reconstruction. Technically SATeCo freezes all the parameters of the pre-trained UNet and VAE and only optimizes two deliberately-designed spatial feature adaptation (SFA) and temporal feature alignment (TFA) modules in the decoder of UNet and VAE. SFA modulates frame features via adaptively estimating affine parameters for each pixel guaranteeing pixel-wise guidance for high-resolution frame synthesis. TFA delves into feature interaction within a 3D local window (tubelet) through self-attention and executes cross-attention between tubelet and its low-resolution counterpart to guide temporal feature alignment. Extensive experiments conducted on the REDS4 and Vid4 datasets demonstrate the effectiveness of our approach.