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ICLR2025Top-tier venue

Depth Any Video with Scalable Synthetic Data

Honghui Yang, Di Huang, Wei Yin, Chunhua Shen, Haifeng Liu, Xiaofei He, Binbin Lin, Wanli Ouyang, Tong He

2025Year
36Top-tier citations

Abstract

Figure 1: We present Depth Any Video, a versatile foundation model supporting both image (top half) and video (bottom half) depth estimation. Derived from Stable Video Diffusion and fine-tuned with diverse and high-quality synthetic data, our model achieves remarkably robust generalization across various real and synthetic unseen scenarios. Additionally, it faithfully captures intricate fine-grained details while ensuring temporal consistency throughout the video.

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