DepthCrafter

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

_**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

CVPR 2025， Highlight

🔆 Notice

DepthCrafter is still under active development!

We recommend that everyone use English to communicate on issues, as this helps developers from around the world discuss, share experiences, and answer questions together.

For business licensing and other related inquiries, don't hesitate to contact wbhu@tencent.com.

🔆 Introduction

🤗 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.

• [25-12-01] Refactored the codebase for better usability and extensibility.
• [25-04-05] 🔥🔥🔥 Its upgraded work, GeometryCrafter, is released now, for video to point cloud!
• [25-04-05] 🎉🎉🎉 DepthCrafter is selected as Highlight in CVPR‘25.
• [24-12-10] 🌟🌟🌟 EXR output format is supported now, with --save_exr option.
• [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!

📦 Release Notes

• 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

🎥 Visualization

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

https://github.com/user-attachments/assets/62141cc8-04d0-458f-9558-fe50bc04cc21

🚀 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:

cd DepthCrafter
uv venv
source .venv/bin/activate
uv sync
uv pip list

🤗 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:

• ~2.1 fps on A100, recommended for high-quality results:

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

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

• ~8.6 fps on A100:

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

🚀 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 path.)
• Evaluation for all datasets scripts:

  bash benchmark/eval/eval.sh
  

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

🤝🍻 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.

  Contributors

🧪 Testing

We provide comprehensive unit tests to ensure code quality and reliability.

Running Tests

1. Run all tests:

pytest unit_tests/

2. Run tests with verbose output:

pytest unit_tests/ -v

3. Run specific test file:

pytest unit_tests/test_depth_crafter_ppl.py

Test Structure

• unit_tests/test_depth_crafter_ppl.py: Tests for the main depth estimation pipeline
• unit_tests/test_inference.py: Tests for the inference interface
• unit_tests/test_utils.py: Tests for utility functions
• unit_tests/test_unet.py: Tests for the UNet model

Requirements

• GPU with CUDA support is required for test_pipeline_gpu_integration
• Tests use small tensor sizes to minimize memory usage
• All heavy computations are mocked for fast execution

Star History

📜 Citation

If you find this work helpful, please consider citing:

@inproceedings{hu2025-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},
            booktitle   = {CVPR},
            year        = {2025}
    }