Search papers, labs, and topics across Lattice.
TAMF-VTON introduces a novel mask-free framework for virtual try-on that addresses the limitations of existing methods by enabling high-fidelity image synthesis without the need for segmentation masks. The approach leverages a lightweight Mixture-of-Experts adaptation, frequency-domain supervision for texture preservation, and a robust data curation pipeline to support diverse garment compositions while maintaining body structure. Extensive experiments reveal that TAMF-VTON significantly outperforms state-of-the-art techniques in both quantitative metrics and perceptual quality, achieving efficient inference times suitable for real-world applications.
Mask-free virtual try-on can now preserve intricate textures and support multiple garments simultaneously, revolutionizing digital fashion experiences.
Recent diffusion-based virtual try-on (VTON) methods remain limited by their reliance on segmentation masks, insufficient preservation of fine-grained textures, and limited support for arbitrary multi-garment compositions. Consequently, existing approaches still face significant challenges in real-world e-commerce deployment. We present TAMF-VTON, a texture-aware, mask-free framework that enables high-fidelity image synthesis under practical unconstrained conditions. Our method requires no human parsing or inpainting masks at inference time and supports diverse garment styles, categories, and quantities, enabling the simultaneous transfer of multiple items while preserving body structure and intricate texture details. This is achieved through a unified generative pipeline with three key components: (1) a lightweight Mixture-of-Experts (MoE) adaptation scheme that enables efficient fine-tuning without compromising the base model's general editing capabilities; (2) a frequency-domain supervision mechanism that explicitly optimizes high-frequency spectral consistency to preserve high-fidelity textures; and (3) a robust data curation pipeline employing an adaptive inpainting strategy to simulate the inverse VTON process for high-quality training pair generation. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods in both quantitative metrics and perceptual quality. Optimized for efficiency, the model achieves inference in under 15 seconds per image on an NVIDIA RTX 4090 with INT4 quantization. By combining mask-free operation, flexible multi-garment composition, faithful texture preservation, and efficient inference on consumer hardware, TAMF-VTON demonstrates a commercially viable solution for scalable deployment in real-world digital fashion scenarios. The project is available at https://www.style3d.ai/ai-photoshoot/virtual-clothing-try-on.
