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The Diffusion model has a strong ability to generate wild images. However,the model can just generate inaccurate images with the guidance of text, whichmakes it very challenging to directly apply the text-guided generative modelfor virtual try-on scenarios. Taking images as guiding conditions of thediffusion model, this paper proposes a brand new personalized virtual try-onmodel (PE-VITON), which uses the two stages (shape control and textureguidance) to decouple the clothing attributes. Specifically, the proposed modeladaptively matches the clothing to human body parts through the Shape ControlModule (SCM) to mitigate the misalignment of the clothing and the human bodyparts. The semantic information of the input clothing is parsed by the TextureGuided Module (TGM), and the corresponding texture is generated by directionalguidance. Therefore, this model can effectively solve the problems of weakreduction of clothing folds, poor generation effect under complex humanposture, blurred edges of clothing, and unclear texture styles in traditionaltry-on methods. Meanwhile, the model can automatically enhance the generatedclothing folds and textures according to the human posture, and improve theauthenticity of virtual try-on. In this paper, qualitative and quantitativeexperiments are carried out on high-resolution paired and unpaired datasets,the results show that the proposed model outperforms the state-of-the-artmodel.

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