TAMF-VTON: Mask-Free Virtual Try-On with Texture Preservation
TAMF-VTON is a new diffusion-based virtual try-on framework that eliminates the need for segmentation masks at inference, supports multi-garment composition, and preserves fine-grained textures. The method achieves inference in under 15 seconds per image on consumer hardware with INT4 quantization and outperforms state-of-the-art methods in both quantitative metrics and perceptual quality. TAMF-VTON introduces a unified generative pipeline with a Mixture-of-Experts adaptation scheme, frequency-domain supervision for texture fidelity, and a robust data curation process.
Why it matters: This work addresses key barriers to real-world e-commerce deployment of virtual try-on by removing mask requirements and enabling efficient, high-fidelity multi-garment transfer on consumer GPUs.
Full story at: arXiv Computer Vision ↗