LiteMP-VTON: A Knowledge-Distilled Diffusion Model for Realistic and Efficient Virtual Try-On
Year: 2025
Type: article
Abstract: Diffusion-based approaches have recently emerged as powerful alternatives to GAN-based virtual try-on methods, offering improved detail preservation and visual realism. Despite their advantages, the substantial number of parameters and intensive computational requirements pose significant barriers to deployment on low-resource platforms. To tackle these limitations, we propose a diffusion-based virtual try-on framework optimized through feature-level knowledge compression. Our method introduces MP-VTON, an enhanced inpainting pipeline based on Stable Diffusion, which incorporates improved Masking techniques and Pose-conditioned enhancement to alleviate garment boundary artifacts. To reduce model size while maintaining performance, we adopt an attention-guided distillation strategy that transfers semantic and structural knowledge from MP-VTON to a lightweight model, LiteMP-VTON. Experiments demonstrate that LiteMP-VTON achieves nearly a 3× reduction in parameter count and close to 2× speedup in inference, making it well suited for deployment in resource-limited environments without significantly compromising generation quality.
Source: Information
DOI: 10.3390/info16050408
Authors: Shufang Zhang, Lei Wang, Wenxin Ding
Institution: Tianjin University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W4410400595