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
2025年份
36顶会引用
摘要
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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引用它的顶会 Paper36
- Pixel-Perfect Depth with Semantics-Prompted Diffusion TransformersGangwei Xu, Haotong Lin, Hongcheng Luo, Xianqi Wang 等NeurIPS 2025 · 被引用 58 次
- OmniWorld: A Multi-Domain and Multi-Modal Dataset for 4D World ModelingYang Zhou, Yifan Wang, Jianjun Zhou, Wenzheng Chang 等ICLR 2026 · 被引用 58 次
- DICEPTION: A Generalist Diffusion Model for Visual Perceptual TasksCanyu Zhao, Yanlong Sun, Mingyu Liu, Huanyi Zheng 等NeurIPS 2025 · 被引用 45 次
- NeoVerse: Enhancing 4D World Model with in-the-wild Monocular VideosYuxue Yang, Lue Fan, Ziqi Shi, Junran Peng 等CVPR 2026 · 被引用 42 次
- GeoVideo: Introducing Geometric Regularization into Video Generation ModelYunpeng Bai, Shaoheng Fang, Chaohui Yu, Fan Wang 等NeurIPS 2025 · 被引用 18 次
它引用的顶会 Paper32
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