Learning Temporally Consistent Video Depth from Video Diffusion Priors

• URL: http://arxiv.org/abs/2406.01493v3
• Date: Mon, 02 Dec 2024 17:10:34 GMT
• Title: Learning Temporally Consistent Video Depth from Video Diffusion Priors
• Authors: Jiahao Shao, Yuanbo Yang, Hongyu Zhou, Youmin Zhang, Yujun Shen, Vitor Guizilini, Yue Wang, Matteo Poggi, Yiyi Liao,
• Abstract summary: This work addresses the challenge of streamed video depth estimation. We argue that sharing contextual information between frames or clips is pivotal in fostering temporal consistency. Specifically, we propose a consistent context-aware training and inference strategy for arbitrarily long videos to provide cross-clip context.
• Score: 62.36887303063542
• License:
• 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. Thus, instead of directly developing a depth estimator from scratch, we reformulate this predictive task 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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