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README.md
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[Sijie Zhao<sup>1</sup>](https://scholar.google.com/citations?user=tZ3dS3MAAAAJ&hl=en),
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[Xiaodong Cun<sup>1</sup>](https://vinthony.github.io/academic), <br>
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[Yong Zhang<sup>1</sup>](https://yzhang2016.github.io),
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[Long Quan<sup>2</sup>](https://home.cse.ust.hk/~quan),
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[Ying Shan<sup>3, 1</sup>](https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=en)**_
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<br><br>
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<sup>1</sup>Tencent AI Lab
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<sup>2</sup>The Hong Kong University of Science and Technology
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<sup>3</sup>ARC Lab, Tencent PCG
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arXiv preprint, 2024
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</div>
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## π Introduction
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- [24-9-19] Add scripts for preparing benchmark datasets.
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- [24-9-18] Add point cloud sequence visualization.
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- [24-9-14] π₯π₯π₯ **DepthCrafter** is released now, have fun!
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π€ DepthCrafter can generate temporally consistent long depth sequences with fine-grained details for open-world videos,
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without requiring additional information such as camera poses or optical flow.
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## π₯ Visualization
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We provide some demos of unprojected point cloud sequences, with reference RGB and estimated depth videos.
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Please refer to our [project page](https://depthcrafter.github.io) for more details.
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https://github.com/user-attachments/assets/62141cc8-04d0-458f-9558-fe50bc04cc21
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## π Quick Start
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### π οΈ Installation
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1. Clone this repo:
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```bash
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git clone https://github.com/Tencent/DepthCrafter.git
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```
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2. Install dependencies (please refer to [requirements.txt](requirements.txt)):
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```bash
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pip install -r requirements.txt
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```
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## π€ Model Zoo
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[DepthCrafter](https://huggingface.co/tencent/DepthCrafter) is available in the Hugging Face Model Hub.
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### πββοΈ Inference
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#### 1. High-resolution inference, requires a GPU with ~26GB memory for 1024x576 resolution:
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- Full inference (~0.6 fps on A100, recommended for high-quality results):
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```bash
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python run.py --video-path examples/example_01.mp4
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```
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- Fast inference through 4-step denoising and without classifier-free guidance οΌ~2.3 fps on A100οΌ:
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```bash
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python run.py --video-path examples/example_01.mp4 --num-inference-steps 4 --guidance-scale 1.0
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```
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#### 2. Low-resolution inference, requires a GPU with ~9GB memory for 512x256 resolution:
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- Full inference (~2.3 fps on A100):
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```bash
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python run.py --video-path examples/example_01.mp4 --max-res 512
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```
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- Fast inference through 4-step denoising and without classifier-free guidance (~9.4 fps on A100):
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```bash
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python run.py --video-path examples/example_01.mp4 --max-res 512 --num-inference-steps 4 --guidance-scale 1.0
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```
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## π€ Gradio Demo
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We provide a local Gradio demo for DepthCrafter, which can be launched by running:
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```bash
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gradio app.py
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```
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## π€ Contributing
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- Welcome to open issues and pull requests.
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- Welcome to optimize the inference speed and memory usage, e.g., through model quantization, distillation, or other acceleration techniques.
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## π Citation
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If you find this work helpful, please consider citing:
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```bibtex
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@article{hu2024-DepthCrafter,
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author = {Hu, Wenbo and Gao, Xiangjun and Li, Xiaoyu and Zhao, Sijie and Cun, Xiaodong and Zhang, Yong and Quan, Long and Shan, Ying},
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title = {DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos},
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journal = {arXiv preprint arXiv:2409.02095},
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year = {2024}
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}
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```
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---
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title: DepthCrafter
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emoji: π¦
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colorFrom: purple
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colorTo: pink
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sdk: gradio
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sdk_version: 4.44.0
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app_file: app.py
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pinned: false
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license: other
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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if __name__ == "__main__":
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demo.queue()
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demo.launch(server_name="0.0.0.0", server_port=80, debug=True)
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if __name__ == "__main__":
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demo.queue()
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# demo.launch(server_name="0.0.0.0", server_port=80, debug=True)
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demo.launch(share=True)
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