CAT-DM: Controllable Accelerated Virtual Try-on with Diffusion Model

Jianhao Zeng; Dan Song; Weizhi Nie; Hongshuo Tian; Tongtong Wang; Anan Liu

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arXiv · 2311.18405

CAT-DM: Controllable Accelerated Virtual Try-on with Diffusion Model

Jianhao Zeng·Dan Song·Weizhi Nie·Hongshuo Tian·Tongtong Wang·Anan Liu

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Abstract

Generative Adversarial Networks (GANs) dominate the research field in image-based virtual try-on, but have not resolved problems such as unnatural deformation of garments and the blurry generation quality. While the generative quality of diffusion models is impressive, achieving controllability poses a significant challenge when applying it to virtual try-on and multiple denoising iterations limit its potential for real-time applications. In this paper, we propose Controllable Accelerated virtual Try-on with Diffusion Model (CAT-DM). To enhance the controllability, a basic diffusion-based virtual try-on network is designed, which utilizes ControlNet to introduce additional control conditions and improves the feature extraction of garment images. In terms of acceleration, CAT-DM initiates a reverse denoising process with an implicit distribution generated by a pre-trained GAN-based model. Compared with previous try-on methods based on diffusion models, CAT-DM not only retains the pattern and texture details of the inshop garment but also reduces the sampling steps without compromising generation quality. Extensive experiments demonstrate the superiority of CAT-DM against both GANbased and diffusion-based methods in producing more realistic images and accurately reproducing garment patterns.

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Jianhao Zeng, Dan Song, Weizhi Nie, Hongshuo Tian, Tongtong Wang, Anan Liu. 2023-11-30. CAT-DM: Controllable Accelerated Virtual Try-on with Diffusion Model. https://arxiv.org/abs/2311.18405

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