✨ TPC: Test-time Procrustes Calibration for Diffusion-based Human Image Animation

NeurIPS 2024

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🎬 Teaser Demo

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🚀 Introduction

TPC is a framework that enhances the fidelity of human image animation by addressing compositional misalignment (e.g., scale, rotation) between reference and target images.
Our method provides a calibrated reference image for the diffusion model, making it robust to real-world misalignment scenarios.

• ✅ Model-agnostic: works with any diffusion-based human image animation system
• ✅ No training required: applied only at test time
• ✅ Improves fidelity & consistency under misaligned conditions

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Human image animation aims to generate a human motion video from the inputs of a reference human image and a target motion video. Current diffusion-based image animation systems exhibit high precision in transferring human identity into targeted motion, yet they still exhibit irregular quality in their outputs. Their optimal precision is achieved only when the physical compositions (i.e., scale and rotation) of the human shapes in the reference image and target pose frame are aligned. In the absence of such alignment, there is a noticeable decline in fidelity and consistency. Especially, in real-world environments, this compositional misalignment commonly occurs, posing significant challenges to the practical usage of current systems. To this end, we propose Test-time Procrustes Calibration (TPC), which enhances the robustness of diffusion-based image animation systems by maintaining optimal performance even when faced with compositional misalignment, effectively addressing real-world scenarios. The TPC provides a calibrated reference image for the diffusion model, enhancing its capability to understand the correspondence between human shapes in the reference and target images. Our method is simple and can be applied to any diffusion-based image animation system in a model-agnostic manner, improving the effectiveness at test time without additional training.

⚙️ Installation

Environment for TPC

conda create -n openmmlab_my python=3.8
conda activate openmmlab_my
pip install -r requirements.txt

If it is not working, try below

conda create -n openmmlab_my python=3.8
conda activate openmmlab_my
pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2

2. (https://detectron2.readthedocs.io/en/latest/tutorials/install.html) SAM
git clone https://github.com/facebookresearch/detectron2.git
python -m pip install -e detectron2

3. (https://mmpose.readthedocs.io/en/latest/installation.html) Openmm
pip install -U openmim
mim install mmengine
mim install "mmcv>=2.0.1"
mim install "mmdet>=3.1.0"
mim install "mmpose>=1.1.0"

TPC: Prepare Calibrated images for Human Image Animation model

Input:

• sample1.PNG (reference image)
• sample1.mp4 (driving video)

Output:

• results/cal_image/pose_sample1_ref_sample1
  • 0-person_cal_ref_img
  • 0-person_cal_ref_mask_img
  • 0-person_pose_mask_img
  • 0-person_ref_mask_img
  • pose_pad_mask_img
  • poes_video
  • ref_img

cd TPC
python run.py

Environment for Human Image Animation model (e.g., MagicAnimate)

magicanimate

Inference Human Image Animation model with TPC

move calibrated images to Human Image Animation model (e.g., MagicAnimate)

Type target prompt in config/config_sample.yaml to get edited video.

cp TPC/TPC/results/cal_image/pose_sample1_ref_sample1 TPC/model/magic-animate_TPC/inputs/applications/calibrated_image/
cd model/magicanimate_TPC
bash scripts/animate.sh

Acknowledgement

This code is implemented on top of following contributions: magicanimate, SAM, DensePose, MMPose

We thank the authors for open-sourcing these great projects and papers!

This work was supported by Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.RS-2021-II211381, Development of Causal AI through Video Understanding and Reinforcement Learning, and Its Applications to Real Environments) and partly supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.RS-2022-II220184, 2022-0-00184, Development and Study of AI Technologies to Inexpensively Conform to Evolving Policy on Ethics)

Citation

Please kindly cite our paper if you use our code, data, models or results:

@article{yoon2024tpc,
  title={Tpc: Test-time procrustes calibration for diffusion-based human image animation},
  author={Yoon, Sunjae and Koo, Gwanhyeong and Lee, Younghwan and Yoo, Chang},
  journal={Advances in Neural Information Processing Systems},
  volume={37},
  pages={118654--118677},
  year={2024}
}