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arXiv · 2406.01493

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
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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/.

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Jiahao Shao, Yuanbo Yang, Hongyu Zhou, Youmin Zhang, Yujun Shen, Vitor Guizilini, Yue Wang, Matteo Poggi, Yiyi Liao. 2024-06-03. Learning Temporally Consistent Video Depth from Video Diffusion Priors. https://arxiv.org/abs/2406.01493

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