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NormalCrafter: Learning Temporally Consistent Video Normal from Video Diffusion Priors


Yanrui Bin1,Wenbo Hu2*, Haoyuan Wang3, Xinya Chen4, Bing Wang2 †
1Spatial Intelligence Group, The Hong Kong Polytechnic University 2ARC Lab, Tencent PCG 3City University of Hong Kong 4Huazhong University of Science and Technology

ICCV 2025

Version        

🔆 Notice

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 binyanrui@gmail.com.

🔆 Introduction

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

🔥 NormalCrafter can generate temporally consistent normal sequences with fine-grained details from open-world videos with arbitrary lengths.

  • [24-04-01] 🔥🔥🔥 NormalCrafter is released now, have fun!

🚀 Quick Start

🤖 Gradio Demo

🛠️ Installation

  1. Clone this repo:
git clone git@github.com:Binyr/NormalCrafter.git
  1. Install dependencies (please refer to requirements.txt):
pip install -r requirements.txt

🤗 Model Zoo

NormalCrafter is available in the Hugging Face Model Hub.

🏃‍♂️ Inference

1. High-resolution inference, requires a GPU with ~20GB memory for 1024x576 resolution:

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

2. Low-resolution inference requires a GPU with ~6GB memory for 512x256 resolution:

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

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[ICCV 2025] NormalCrafter: Learning Temporally Consistent Video Normal from Video Diffusion Priors

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