Learning Temporally Consistent Video Depth from Video Diffusion Priors

Jiahao Shao; Yuanbo Yang; Hongyu Zhou; Youmin Zhang; Yujun Shen; Vitor Guizilini; Yue Wang; Matteo Poggi; Yiyi Liao

Paper

Learning Temporally Consistent Video Depth from Video Diffusion Priors

Abstract

This work addresses the challenge of streamed video depth estimation, which expects not only per-frame accuracy but, more importantly, cross-frame consistency. We argue that sharing contextual information between frames or clips is pivotal in fostering temporal consistency. Therefore, we reformulate depth prediction into a conditional generation problem to provide contextual information within a clip and across clips. Specifically, we propose a consistent context-aware training and inference strategy for arbitrarily long videos to provide cross-clip context. We sample independent noise levels for each frame within a clip during training while using a sliding window strategy and initializing overlapping frames with previously predicted frames without adding noise. Moreover, we design an effective training strategy to provide context within a clip. Extensive experimental results validate our design choices and demonstrate the superiority of our approach, dubbed ChronoDepth. Project page: https://xdimlab.github.io/ChronoDepth/.