CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models
<|reference_start|>CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models: Virtual try-on methods based on diffusion models achieve realistic try-on effects but often replicate the backbone network as a ReferenceNet or use additional image encoders to process condition inputs, leading to high training and inference costs. In this work, we rethink the necessity of ReferenceNet and image encoders and innovate the interaction between garment and person by proposing CatVTON, a simple and efficient virtual try-on diffusion model. CatVTON facilitates the seamless transfer of in-shop or worn garments of any category to target persons by simply concatenating them in spatial dimensions as inputs. The efficiency of our model is demonstrated in three aspects: (1) Lightweight network: Only the original diffusion modules are used, without additional network modules. The text encoder and cross-attentions for text injection in the backbone are removed, reducing the parameters by 167.02M. (2) Parameter-efficient training: We identified the try-on relevant modules through experiments and achieved high-quality try-on effects by training only 49.57M parameters, approximately 5.51 percent of the backbone network's parameters. (3) Simplified inference: CatVTON eliminates all unnecessary conditions and preprocessing steps, including pose estimation, human parsing, and text input, requiring only a garment reference, target person image, and mask for the virtual try-on process. Extensive experiments demonstrate that CatVTON achieves superior qualitative and quantitative results with fewer prerequisites and trainable parameters than baseline methods. Furthermore, CatVTON shows good generalization in in-the-wild scenarios despite using open-source datasets with only 73K samples.<|reference_end|>
@article{chong2024catvton:,
    title={CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion
  Models},
    author={Zheng Chong, Xiao Dong, Haoxiang Li, Shiyue Zhang, Wenqing Zhang,
  Xujie Zhang, Hanqing Zhao and Xiaodan Liang},
    journal={arXiv preprint arXiv:2407.15886},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15886},
    primaryClass={cs.CV cs.AI}
}