Eevee
[CVPR 2026 Findings] Eevee: Towards Close-up High-resolution Video-based Virtual Try-on
Install / Use
/learn @AMAP-ML/EeveeREADME
Abstract
Video virtual try-on technology provides a cost-effective solution for creating marketing videos in fashion e-commerce. However, its practical adoption is hindered by two critical limitations. First, the reliance on a single garment image as input in current virtual try-on datasets limits the accurate capture of realistic texture details. Second, most existing methods focus solely on generating full-shot virtual try-on videos, neglecting the business's demand for videos that also provide detailed close-ups. To address these challenges, we introduce a high-resolution dataset for video-based virtual try-on. This dataset offers two key features. First, it provides more detailed information on the garments, which includes high-fidelity images with detailed close-ups and textual descriptions; Second, it uniquely includes full-shot and close-up try-on videos of real human models. Furthermore, accurately assessing consistency becomes significantly more critical for the close-up videos, which demand high-fidelity preservation of garment details. To facilitate such fine-grained evaluation, we propose a new garment consistency metric VGID (Video Garment Inception Distance) that quantifies the preservation of both texture and structure. Our experiments validate these contributions. We demonstrate that by utilizing the detailed images from our dataset, existing video generation models can extract and incorporate texture features, significantly enhancing the realism and detail fidelity of virtual try-on results. Furthermore, we conduct a comprehensive benchmark of recent models. The benchmark effectively identifies the texture and structural preservation problems among current methods.
Preparation
- Environment
We recommend using Anaconda to manage your environment. Please ensure you have CUDA 12.4 or higher installed.
# 1. Clone the repository
git clone https://github.com/AMAP-ML/Eevee.git
cd Eevee
# 2， Create conda environment and activate it
conda create -n eevee python=3.10 -y
conda activate eevee
# 3. Install PyTorch for CUDA 12.4
pip install torch==2.5.1 torchvision==0.20.1 --index-url https://download.pytorch.org/whl/cu124
# 4. Install other dependencies
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
# 5. Install flash attention for acceleration (Optionally)
wget https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.4.post1/flash_attn-2.7.4.post1+cu12torch2.5cxx11abiFALSE-cp310-cp310-linux_x86.whl
pip install flash_attn-2.7.4.post1+cu12torch2.5cxx11abiFALSE-cp310-cp310-linux_x86.whl
- Pre-trained weights
Eevee requires the Wan2.1-VACE checkpoints for training. Please download the weights as follows:
# 1. Sets the environment variable to point to a mirror site for faster and more stable Hugging Face connections (Optionally)
export HF_ENDPOINT=https://hf-mirror.com
# 2. Download the weights
python ./utils/download_vace.py
- Dataset access
The Eevee dataset is a core contribution of our work. Please download it as follows
# 1. Sets the environment variable to point to a mirror site for faster and more stable Hugging Face connections (Optionally)
export HF_ENDPOINT=https://hf-mirror.com
# 2. Download the dataset
python ./utils/download_dataset.py
# 3. Merges the split multi-part files into a single zip archive and extracts the contents
cd ./data
cat Eevee.zip.part* > Eevee.zip
unzip Eevee.zip -d ./Eevee
cd ..
- Recommended Directory Structure
After downloading, your project structure should look like this:
Eevee
|-- checkpoints/
| |-- Wan2.1-VACE-14B/
| | |-- google/
| | | |-- umt5-xxl/
| | | | ...
| | |-- config.json
| | |-- Wan2.1_VAE.pth
| | |-- diffusion_pytorch_model-00001-of-00007.safetensors
| | |-- diffusion_pytorch_model-00002-of-00007.safetensors
| | |-- diffusion_pytorch_model-00003-of-00007.safetensors
| | |-- diffusion_pytorch_model-00004-of-00007.safetensors
| | |-- diffusion_pytorch_model-00005-of-00007.safetensors
| | |-- diffusion_pytorch_model-00006-of-00007.safetensors
| | |-- diffusion_pytorch_model-00007-of-00007.safetensors
| | |-- models_t5_umt5-xxl-enc-bf16.pth
| | ...
|-- data/
| |-- Eevee/
| | |-- dresses/
| | | |-- 00030/
| | | | |-- garment_caption.txt
| | | | |-- garment_detail.png
| | | | |-- garment_line.png
| | | | |-- garment_mask.png
| | | | |-- garment.png
| | | | |-- person_agnostic.png
| | | | |-- person_mask.png
| | | | |-- person.png
| | | | |-- video_0_agnostic_sam.mp4
| | | | |-- video_0_agnostic.mp4
| | | | |-- video_0_densepose.mp4
| | | | |-- video_0_mask.mp4
| | | | |-- video_0.mp4
| | | | |-- video_1_agnostic_sam.mp4
| | | | |-- video_1_agnostic.mp4
| | | | |-- video_1_densepose.mp4
| | | | |-- video_1_mask.mp4
| | | | |-- video_1.mp4
| | | |-- 00032/
| | | ...
| | |-- lower_body/
| | | |-- 00003/
| | | ...
| | |-- upper_bdoy/
| | | |-- 00000/
| | | ...
| | |-- dresses_test.csv
| | |-- dresses_train.csv
| | |-- lower_test.csv
| | |-- lower_train.csv
| | |-- upper_test.csv
| | |-- upper_train.csv
|-- assets/
| | ...
|-- dataset/
| | ...
|-- models/
| | ...
|-- test/
| | ...
|-- train/
| | ...
|-- utils/
| | ...
|-- requirements.txt
|-- README.md
Training
bash train/train.sh
Testing
bash test/test.sh
