DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos

     

Wenbo Hu^{1* †}, Xiangjun Gao^2*, Xiaoyu Li^{1* †}, Sijie Zhao¹, Xiaodong Cun¹,
Yong Zhang¹, Long Quan², Ying Shan<sup>3, 1

1</sup>Tencent AI Lab ²The Hong Kong University of Science and Technology ³ARC Lab, Tencent PCG

arXiv preprint, 2024

🔆 Introduction

• [24-10-19] 🤗🤗🤗 DepthCrafter now has been integrated into ComfyUI!
• [24-10-08] 🤗🤗🤗 DepthCrafter now has been integrated into Nuke, have a try!
• [24-09-28] Add full dataset inference and evaluation scripts for better comparison use. :-)
• [24-09-25] 🤗🤗🤗 Add huggingface online demo DepthCrafter.
• [24-09-19] Add scripts for preparing benchmark datasets.
• [24-09-18] Add point cloud sequence visualization.
• [24-09-14] 🔥🔥🔥 DepthCrafter is released now, have fun!

🔥 DepthCrafter can generate temporally consistent long-depth sequences with fine-grained details for open-world videos, without requiring additional information such as camera poses or optical flow.

🤗 If you find DepthCrafter useful, please help ⭐ this repo, which is important to Open-Source projects. Thanks!

🎥 Visualization

We provide demos of unprojected point cloud sequences, with reference RGB and estimated depth videos. Please refer to our project page for more details.

365030500-ff625ffe-93ab-4b58-a62a-50bf75c89a92.mov

🚀 Quick Start

🤖 Gradio Demo

• Online demo: DepthCrafter
• Local demo:

  gradio app.py

🌟 Community Support

• NukeDepthCrafter: a plugin allows you to generate temporally consistent Depth sequences inside Nuke, which is widely used in the VFX industry.
• ComfyUI-Nodes: creating consistent depth maps for your videos using DepthCrafter in ComfyUI.

🛠️ Installation

1. Clone this repo:

git clone https://github.com/Tencent/DepthCrafter.git

2. Install dependencies (please refer to requirements.txt):

pip install -r requirements.txt

🤗 Model Zoo

DepthCrafter is available in the Hugging Face Model Hub.

🏃‍♂️ Inference

1. High-resolution inference, requires a GPU with ~26GB memory for 1024x576 resolution:

• Full inference (~0.6 fps on A100, recommended for high-quality results):

  python run.py  --video-path examples/example_01.mp4

• Fast inference through 4-step denoising and without classifier-free guidance （~2.3 fps on A100）:

  python run.py  --video-path examples/example_01.mp4 --num-inference-steps 4 --guidance-scale 1.0

2. Low-resolution inference requires a GPU with ~9GB memory for 512x256 resolution:

• Full inference (~2.3 fps on A100):

  python run.py  --video-path examples/example_01.mp4 --max-res 512

• Fast inference through 4-step denoising and without classifier-free guidance (~9.4 fps on A100):

  python run.py  --video-path examples/example_01.mp4  --max-res 512 --num-inference-steps 4 --guidance-scale 1.0

🚀 Dataset Evaluation

Please check the benchmark folder.

• To create the dataset we use in the paper, you need to run dataset_extract/dataset_extract_${dataset_name}.py.
• Then you will get the csv files that save the relative root of extracted RGB video and depth npz files. We also provide these csv files.
• Inference for all datasets scripts:

  bash benchmark/infer/infer.sh

  (Remember to replace the input_rgb_root and saved_root with your own path.)
• Evaluation for all datasets scripts:

  bash benchmark/eval/eval.sh

  (Remember to replace the pred_disp_root and gt_disp_root with your own path.)

🤝 Contributing

• Welcome to open issues and pull requests.
• Welcome to optimize the inference speed and memory usage, e.g., through model quantization, distillation, or other acceleration techniques.

📜 Citation

If you find this work helpful, please consider citing:

@article{hu2024-DepthCrafter,
            author      = {Hu, Wenbo and Gao, Xiangjun and Li, Xiaoyu and Zhao, Sijie and Cun, Xiaodong and Zhang, Yong and Quan, Long and Shan, Ying},
            title       = {DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos},
            journal     = {arXiv preprint arXiv:2409.02095},
            year        = {2024}
    }