Summary of Depth Any Video with Scalable Synthetic Data, by Honghui Yang et al.
Depth Any Video with Scalable Synthetic Data
by Honghui Yang, Di Huang, Wei Yin, Chunhua Shen, Haifeng Liu, Xiaofei He, Binbin Lin, Wanli Ouyang, Tong He
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to video depth estimation, called Depth Any Video, is introduced in this paper. The model addresses the scarcity of consistent ground truth data by developing a synthetic data pipeline that generates 40,000 video clips with precise depth annotations. Additionally, it leverages generative video diffusion models and advanced techniques like rotary position encoding and flow matching to handle real-world videos efficiently. The model’s mixed-duration training strategy allows it to perform robustly across different frame rates and sequences of varying lengths. At inference, a depth interpolation method is proposed for high-resolution video depth estimation up to 150 frames. The paper outperforms previous generative depth models in terms of spatial accuracy and temporal consistency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Depth Any Video is a new way to estimate the depth (distance from the camera) of objects in videos. This helps us understand what’s happening in a video by knowing how far away things are. Right now, it’s hard to find reliable data to train models that can do this well. The authors created a big dataset with 40,000 short videos each with accurate depth information. They also developed a special kind of AI model that can handle different types of videos and even single frames. This model is better than previous ones at estimating depth and keeping track of how things change over time. |
Keywords
» Artificial intelligence » Depth estimation » Inference » Synthetic data
