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README.md

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-license: mit
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+---
+license: mit
+---
+
+# 3DV-TON: Textured 3D-Guided Consistent Video Try-on via Diffusion Models
+Min Wei, Chaohui Yu, Jingkai Zhou, and Fan Wang. 2025.
+3DV-TON: Textured 3D-Guided Consistent Video Try-on via Diffusion Models. 
+In Proceedings of the 33rd ACM International Conference on Multimedia (MM ’25),
+October 27–31, 2025, Dublin, Ireland. ACM, New York, NY, USA, 10 pages.
+https://doi.org/10.1145/3746027.3754754
+
+[![arXiv](https://img.shields.io/badge/arXiv-2504.17414-b31b1b.svg)](https://arxiv.org/abs/2504.17414)
+[![Project Page](https://img.shields.io/badge/Project-Website-green)](https://2y7c3.github.io/3DV-TON/)
+[![Hugging Face Model](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-yellow)](https://huggingface.co/2y7c3/3DV-TON)
+[![HR-VVT](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-HR_VVT-yellow)](https://huggingface.co/datasets/2y7c3/HR-VVT)
+
+## Installation
+
+```
+git clone https://github.com/2y7c3/3DV-TON.git
+cd 3DV-TON
+pip install -r requirements.txt
+
+cd preprocess/model/DensePose/detectron2/projects/DensePose
+pip install -e .
+
+## install GVHMR
+## see https://github.com/zju3dv/GVHMR/blob/main/docs/INSTALL.md
+## replace GVHMR/hmr4d/utils/vis/renderer.py with our preprocess/renderer.py
+```
+
+### Weights
+
+Download [Stable Diffusion](https://huggingface.co/lambdalabs/sd-image-variations-diffusers), [Motion module](https://huggingface.co/guoyww/animatediff/blob/main/v3_sd15_mm.ckpt),[VAE](https://huggingface.co/stabilityai/sd-vae-ft-mse) and Our [3DV-TON models](https://huggingface.co/2y7c3/3DV-TON) in ``` ./ckpts ```.
+
+Download [Cloth masker](https://huggingface.co/2y7c3/3DV-TON) in ``` ./preprocess/ckpts ```. Then you can use our cloth masker to generate agnostic mask videos for improved try-on results.
+
+## Inference
+We provid three demo examples in ```./demos/``` — run the following commands to test them.
+
+```bash
+python infer.py --config ./configs/inference/demo_test.yaml
+```
+
+Or you can prepare your own example by following the steps below.
+
+``` bash
+# 1. generate agnostic mask (type: 'upper', 'lower', 'overall')
+cd preprocess
+python seg_mask.py --input demos/videos/video.mp4 --output demos/ --type overall
+
+# 2. use GVHMR to generate SMPL video
+
+# 3. use image tryon model to generate tryon image (e.g. CaTVTON)
+
+# 4. generate textured 3d mesh
+
+# 5. modify demo_test.yaml, then run
+python infer.py --config ./configs/inference/demo_test.yaml
+```
+
+## BibTeX
+```text
+@article{wei20253dv,
+  title={3dv-ton: Textured 3d-guided consistent video try-on via diffusion models},
+  author={Wei, Min and Yu, Chaohui and Zhou, Jingkai and Wang, Fan},
+  journal={arXiv preprint arXiv:2504.17414},
+  year={2025}
+}
+```