M&M VTO: Multi-Garment Virtual Try-On and Editing
Luyang Zhu, Yingwei Li, Nan Liu, Hao Peng, Dawei Yang, Ira, Kemelmacher-Shlizerman

TL;DR
M&M VTO introduces a novel diffusion-based model for multi-garment virtual try-on, enabling high-resolution, layout-controlled, and identity-preserving virtual fitting with minimal fine-tuning, advancing the state-of-the-art in virtual try-on technology.
Contribution
The paper presents a single-stage diffusion model with a specialized architecture for multi-garment virtual try-on, improving resolution, identity preservation, and layout control over prior methods.
Findings
Achieves state-of-the-art qualitative and quantitative results.
Enables language-guided and multi-garment try-on.
Requires significantly less fine-tuning data for identity preservation.
Abstract
We present M&M VTO, a mix and match virtual try-on method that takes as input multiple garment images, text description for garment layout and an image of a person. An example input includes: an image of a shirt, an image of a pair of pants, "rolled sleeves, shirt tucked in", and an image of a person. The output is a visualization of how those garments (in the desired layout) would look like on the given person. Key contributions of our method are: 1) a single stage diffusion based model, with no super resolution cascading, that allows to mix and match multiple garments at 1024x512 resolution preserving and warping intricate garment details, 2) architecture design (VTO UNet Diffusion Transformer) to disentangle denoising from person specific features, allowing for a highly effective finetuning strategy for identity preservation (6MB model per individual vs 4GB achieved with, e.g.,…
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Taxonomy
TopicsAdditive Manufacturing and 3D Printing Technologies · Manufacturing Process and Optimization
MethodsDiffusion
