A Two-stage Personalized Virtual Try-on Framework with Shape Control and Texture Guidance

• URL: http://arxiv.org/abs/2312.15480v1
• Date: Sun, 24 Dec 2023 13:32:55 GMT
• Title: A Two-stage Personalized Virtual Try-on Framework with Shape Control and Texture Guidance
• Authors: Shufang Zhang, Minxue Ni, Lei Wang, Wenxin Ding, Shuai Chen, Yuhong Liu
• Abstract summary: This paper proposes a brand new personalized virtual try-on model (PE-VITON), which uses the two stages (shape control and texture guidance) to decouple the clothing attributes. The proposed model can effectively solve the problems of weak reduction of clothing folds, poor generation effect under complex human posture, blurred edges of clothing, and unclear texture styles in traditional try-on methods.
• Score: 7.302929117437442
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Diffusion model has a strong ability to generate wild images. However, the model can just generate inaccurate images with the guidance of text, which makes it very challenging to directly apply the text-guided generative model for virtual try-on scenarios. Taking images as guiding conditions of the diffusion model, this paper proposes a brand new personalized virtual try-on model (PE-VITON), which uses the two stages (shape control and texture guidance) to decouple the clothing attributes. Specifically, the proposed model adaptively matches the clothing to human body parts through the Shape Control Module (SCM) to mitigate the misalignment of the clothing and the human body parts. The semantic information of the input clothing is parsed by the Texture Guided Module (TGM), and the corresponding texture is generated by directional guidance. Therefore, this model can effectively solve the problems of weak reduction of clothing folds, poor generation effect under complex human posture, blurred edges of clothing, and unclear texture styles in traditional try-on methods. Meanwhile, the model can automatically enhance the generated clothing folds and textures according to the human posture, and improve the authenticity of virtual try-on. In this paper, qualitative and quantitative experiments are carried out on high-resolution paired and unpaired datasets, the results show that the proposed model outperforms the state-of-the-art model.

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