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[bibtex]@InProceedings{Tseng_2024_CVPR, author = {Tseng, Chiang and Chen, Chieh-Yun and Shuai, Hong-Han}, title = {Artifact Does Matter! Low-artifact High-resolution Virtual Try-On via Diffusion-based Warp-and-Fuse Consistent Texture}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {8240-8244} }
Artifact Does Matter! Low-artifact High-resolution Virtual Try-On via Diffusion-based Warp-and-Fuse Consistent Texture
Abstract
In virtual try-on technology achieving realistic fitting of clothing on human subjects without sacrificing detail is a significant challenge. Traditional approaches especially those using Generative Adversarial Networks (GANs) often produce noticeable artifacts while diffusion-based methods struggle with maintaining consistent texture and suffer from high computational demands. To overcome these limitations we propose the Low-artifact High-resolution Virtual Try-on via Diffusion-based Warp-and-Fuse Consistent Texture (LA-VTON). This novel framework introduces Conditional Texture Warping (CTW) and Conditional Texture Fusing (CTF) modules. CTW improves warping stability through simplified denoising steps and CTF ensures texture consistency and enhances computational efficiency achieving inference times 17x faster than existing diffusion-based methods. Experiments show that LA-VTON surpasses current SOTA high-resolution virtual try-on methods in both visual quality and efficiency marking a significant advancement in high-resolution virtual try-on and setting a new standard in digital fashion realism.
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