SIFT-VTON: Geometric Correspondence Supervision on Cross-Attention for Virtual Try-On
Diffusion-based virtual try-on methods achieve photorealistic synthesis through cross-attention mechanisms that transfer garment features to target body regions. However, these approaches rely on implicit learning of spatial correspondences, struggling to preserve fine details such as text and illustrations. We propose a novel approach, which we call SIFT-VTON, that utilizes SIFT keypoint matching to provide explicit geometric guidance for diffusion-based virtual try-on. Our method applies domain-specific filtering to SIFT keypoint matches between garment and person images, then converts these correspondences into spatial probability distributions that supervise cross-attention layers dur...
| Authors | Kosuke Takemoto, Takafumi Koshinaka |
|---|---|
| Primary category | cs.CV |
| Published | 2 May 2026 |
| Last updated at source | 13 August 2026 |
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