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

• URL: http://arxiv.org/abs/2311.18405v2
• Date: Fri, 26 Apr 2024 01:57:00 GMT
• Title: CAT-DM: Controllable Accelerated Virtual Try-on with Diffusion Model
• Authors: Jianhao Zeng, Dan Song, Weizhi Nie, Hongshuo Tian, Tongtong Wang, Anan Liu,
• Abstract summary: Generative Adversarial Networks (GANs) dominate the research field in image-based virtual try-on. We propose Controllable Accelerated virtual Try-on with Diffusion Model (CAT-DM)
• Score: 38.08115084929579
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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