Wenbo Hu1* †,
Xiangjun Gao2*,
Xiaoyu Li1* †,
Sijie Zhao1,
Xiaodong Cun1,
Yong Zhang1,
Long Quan2,
Ying Shan3, 1
1Tencent AI Lab
2The Hong Kong University of Science and Technology
3ARC Lab, Tencent PCG
arXiv preprint, 2024
🤗 If you find DepthCrafter useful, please help ⭐ this repo, which is important to Open-Source projects. Thanks!
🔥 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.
[24-11-26]
🚀🚀🚀 DepthCrafter v1.0.1 is released now, with improved quality and speed[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 v1.0.1:
- Quality and speed improvement
Method ms/frame↓ @1024×576 Sintel (~50 frames) Scannet (90 frames) KITTI (110 frames) Bonn (110 frames) AbsRel↓ δ₁ ↑ AbsRel↓ δ₁ ↑ AbsRel↓ δ₁ ↑ AbsRel↓ δ₁ ↑ Marigold 1070.29 0.532 0.515 0.166 0.769 0.149 0.796 0.091 0.931 Depth-Anything-V2 180.46 0.367 0.554 0.135 0.822 0.140 0.804 0.106 0.921 DepthCrafter previous 1913.92 0.292 0.697 0.125 0.848 0.110 0.881 0.075 0.971 DepthCrafter v1.0.1 465.84 0.270 0.697 0.123 0.856 0.104 0.896 0.071 0.972
- Quality and speed improvement
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
- Online demo: DepthCrafter
- Local demo:
gradio app.py
- 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.
- Clone this repo:
git clone https://github.com/Tencent/DepthCrafter.git
- Install dependencies (please refer to requirements.txt):
pip install -r requirements.txt
DepthCrafter is available in the Hugging Face Model Hub.
-
~2.1 fps on A100, recommended for high-quality results:
python run.py --video-path examples/example_01.mp4
-
~8.6 fps on A100:
python run.py --video-path examples/example_01.mp4 --max-res 512
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:
(Remember to replace the
bash benchmark/infer/infer.sh
input_rgb_root
andsaved_root
with your own path.) - Evaluation for all datasets scripts:
(Remember to replace the
bash benchmark/eval/eval.sh
pred_disp_root
andgt_disp_root
with your own path.)
-
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.
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}
}