Summary of Depthcrafter: Generating Consistent Long Depth Sequences For Open-world Videos, by Wenbo Hu et al.
DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos
by Wenbo Hu, Xiangjun Gao, Xiaoyu Li, Sijie Zhao, Xiaodong Cun, Yong Zhang, Long Quan, Ying Shan
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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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 This paper presents a novel method called DepthCrafter for estimating video depth in open-world scenarios without requiring supplementary information such as camera poses or optical flow. The model is trained using a three-stage strategy that leverages pre-trained image-to-video diffusion models, allowing it to generate long depth sequences with intricate details and variable lengths up to 110 frames. The method achieves state-of-the-art performance in open-world video depth estimation under zero-shot settings on multiple datasets. Additionally, DepthCrafter enables various downstream applications such as depth-based visual effects and conditional video generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Estimating video depth is important for many real-life applications like movies, games, and virtual reality experiences. This paper introduces a new method called DepthCrafter that can estimate video depth without needing extra information about the camera or movement. The method works by training a model to generate depth sequences from videos, and it can do this for long videos with lots of details. The researchers tested their method on many different types of videos and found that it performed better than other methods in some cases. This technology could be used to make movies and games look more realistic and immersive. |
Keywords
» Artificial intelligence » Depth estimation » Optical flow » Zero shot
