OmniVTON++: Training-Free Universal Virtual Try-On with Principal Pose Guidance

Despite the rapid advancement of Virtual Try-On (VTON) and Try-Off (VTOFF) technologies, existing VTON methods face challenges with fine-grained detail preservation, generalization to complex scenes, complicated pipeline, and efficient inference. To tackle these problems, we propose OmniDiT, an omni Virtual Try-On framework based on the Diffusion Transformer, which combines try-on and try-off tasks into one unified model. Specifically, we first establish a self-evolving data curation pipeline to continuously produce data, and construct a large VTON dataset Omni-TryOn, which contains over 380k diverse and high-quality garment-model-tryon image pairs and detailed text prompts. Then, we employ the token concatenation and design an adaptive position encoding to effectively incorporate multiple reference conditions. To relieve the bottleneck of long sequence computation, we are the first to introduce Shifted Window Attention into the diffusion model, thus achieving a linear complexity. To remedy the performance degradation caused by local window attention, we utilize multiple timestep prediction and an alignment loss to improve generation fidelity. Experiments reveal that, under various complex scenes, our method achieves the best performance in both the model-free VTON and VTOFF tasks and a performance comparable to current SOTA methods in the model-based VTON task.

While diffusion-based virtual try-on has achieved impressive visual realism, most methods treat the task as 2D inpainting, prioritizing texture preservation over physical plausibility. Consequently, they often produce plausible-looking images that fail to reflect authentic garment fit across diverse body shapes. We present FitVTON, a Fit-aware virtual try-on model on different bodies in the wild. FitVTON encodes garment-body size through structured text prompts, and learn from simulated try-on triplets from parameterized garment model. To improve the fitting effects over garment silhouettes, we introduce two auxiliary head to predict the masks for both the garment and the exposed body. We further introduce a texture rectification stage to improve realistic appearance from simulated data. To evaluate the fitting fidelity, we curate a real-world dataset, FittingEffect3K, combining VLM-based scoring protocol. Both subjective and quantitive experiments show that FitVTON demonstrate authentic fitting fidelity, with significant sizing accuracy and shape preservation over state-of-the-art methods while maintaining competitive image quality. Project Page: https://zenoning.github.io/FitVTON/.

Diffusion-based virtual try-on (VTON) often relies on segmentation-derived masks that become unreliable when the reference garment differs substantially from the target person's clothing. Existing methods may also lose fine details and commonly focus on single-garment transfer. We present TAMF-VTON, an inference-time mask-free framework built on Qwen-Image-Edit-2509. Given a person image, one or more garment images, and a text instruction, it performs single- or multi-garment transfer without masks, parsing maps, pose representations, or spatial controls at inference. Its central component is token-wise sparse Mixture-of-Experts adaptation, which provides input-dependent capacity without predefined expert roles. Timestep-weighted frequency supervision regularizes high-frequency reconstruction, while adaptive offline data construction supplies cross-category and cross-silhouette training tuples; neither is used at inference. TAMF-VTON achieves the best scores on most reported paired and unpaired metrics among evaluated VTON baselines on the standard single-garment VITON-HD and DressCode benchmarks. Selected qualitative results illustrate multi-garment composition, subject-to-subject transfer, diverse scenes, body-shape variation, and illumination changes. With INT4 on an RTX 4090, TAMF-VTON generates 1K-resolution images under 15 seconds and 2K-resolution images under 30 seconds. Our project can be accessed via https://www.style3d.ai/ai-photoshoot/virtual-clothing-try-on.