DreamVTON: Customizing 3D Virtual Try-on with Personalized Diffusion Models
<|reference_start|>DreamVTON: Customizing 3D Virtual Try-on with Personalized Diffusion Models: Image-based 3D Virtual Try-ON (VTON) aims to sculpt the 3D human according to person and clothes images, which is data-efficient (i.e., getting rid of expensive 3D data) but challenging. Recent text-to-3D methods achieve remarkable improvement in high-fidelity 3D human generation, demonstrating its potential for 3D virtual try-on. Inspired by the impressive success of personalized diffusion models (e.g., Dreambooth and LoRA) for 2D VTON, it is straightforward to achieve 3D VTON by integrating the personalization technique into the diffusion-based text-to-3D framework. However, employing the personalized module in a pre-trained diffusion model (e.g., StableDiffusion (SD)) would degrade the model's capability for multi-view or multi-domain synthesis, which is detrimental to the geometry and texture optimization guided by Score Distillation Sampling (SDS) loss. In this work, we propose a novel customizing 3D human try-on model, named \textbf{DreamVTON}, to separately optimize the geometry and texture of the 3D human. Specifically, a personalized SD with multi-concept LoRA is proposed to provide the generative prior about the specific person and clothes, while a Densepose-guided ControlNet is exploited to guarantee consistent prior about body pose across various camera views. Besides, to avoid the inconsistent multi-view priors from the personalized SD dominating the optimization, DreamVTON introduces a template-based optimization mechanism, which employs mask templates for geometry shape learning and normal/RGB templates for geometry/texture details learning. Furthermore, for the geometry optimization phase, DreamVTON integrates a normal-style LoRA into personalized SD to enhance normal map generative prior, facilitating smooth geometry modeling.<|reference_end|>
@article{xie2024dreamvton:,
    title={DreamVTON: Customizing 3D Virtual Try-on with Personalized Diffusion
  Models},
    author={Zhenyu Xie and Haoye Dong and Yufei Gao and Zehua Ma and Xiaodan Liang},
    journal={arXiv preprint arXiv:2407.16511},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.16511},
    primaryClass={cs.CV}
}