TryOnDiffusion
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About
TryOnDiffusion proposes a diffusion-based architecture that unifies two UNets (referred to as Parallel-UNet) to generate photorealistic visualizations of a garment on an input person. The key challenge addressed is synthesizing a detail-preserving visualization while warping the garment for significant body pose and shape change. Unlike previous methods that failed to balance detail preservation and effective warping, TryOnDiffusion achieves both in a single network. The Parallel-UNet implicitly warps the garment via a cross-attention mechanism, unifying garment warp and person blend. The overall pipeline involves segmenting the target person and garment, computing pose, inputting these into a 128x128 Parallel-UNet, and then scaling the output up to 1024x1024 using a 256x256 Parallel-UNet and standard super-resolution diffusion.
Platform
Task
Features
• high-resolution output up to 1024x1024
• supports multi-person try-on using the same garment
• implicit garment warping via cross attention
• accommodates significant body shape changes
• accommodates significant body pose changes
• preserves garment details while warping
• utilizes parallel-unet diffusion architecture
• photorealistic virtual try-on generation
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