TL;DR
This work presents MMTryOff, a dedicated multi-category unified modeling framework for the VTOFF task, and proposes a Structure-Detail Co-Optimal approach to collaboratively enhance garment structural integrity and textual details.
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.
Though zero-shot adapters excel in image personalization, they often encounter significant challenges in multi-reference personalized generation, specifically failing to precisely adhere to the spatial layouts described in text prompts and suffering from feature leakage between reference images. To address these two challenges, we propose RIG (Regional Image-prompt Generation), a novel training-free framework. For the first challenge, leveraging Multimodal Large Language Models (MLLMs), we introduce a layout planning binder. Leveraging Chain-of-Thought (CoT) reasoning, this module infers and generates precise global layouts from text prompts, while simultaneously binding reference images to their corresponding regions. For the second, we introduce a satially decoupled diffusion mechanism that isolates feature streams during attention computation. By injecting reference features exclusively into designated regions, this mechanism effectively prevents feature interference between reference images. Extensive experiments demonstrate that RIG significantly outperforms state-of-the-art adapter methods in terms of both personalization fidelity and text-layout alignment.
This work introduces VAR-VTON, a VAR-based VTON model that incorporates garment conditioning and structural guidance for efficient latent-space VTON, and proposes STAR-VTON, a Two-Stage AutoRegressive framework that builds upon VAR-VTON by decoupling latent-space structural synthesis from pixel-space detail recovery.
The key idea is to progressively densify anchored VecSet latents via hierarchical point-shuffle upsampling, increasing spatial capacity for fine-grained geometry modeling and replacing global cross-attention with AVS-Conv, a geometry-aware local aggregation operator operating within local neighborhoods rather than the exhaustive latent set.
Recently, diffusion-based removal methods have achieved promising visual quality in removing both target objects and their associated effects. However, they typically rely on multi-step denoising, leading to high inference cost. Directly applying existing one-step distillation methods is also suboptimal, since their global objectives lack explicit region-wise calibration and may weaken the asymmetric edit-and-preserve behavior required by object-effect removal. To address these challenges, we propose TurboClear, a one-step SDXL-based object-effect removal model. During training, we design Region-Calibrated Distribution Matching (RDM) for region-aware distillation to preserve the teacher model's asymmetric edit-and-preserve behavior. Furthermore, we propose Learnable Spatial Fusion (LSF) for lightweight inference-time fusion. Extensive experiments show that TurboClear significantly improves inference efficiency while maintaining competitive visual quality. TurboClear reduces the computational overhead by up to $40.04\times$ compared to ObjectClear, and by up to $665\times$ against the Flux-based method OmniPaint, all while maintaining comparable or better visual removal quality. Code is available at https://github.com/GuoCalix/TurboClear.
We propose a high-capacity, end-to-end framework for large-scale image compression that addresses the trade-off between tiling scalability and perceptual quality, a challenge stemming from the patch-based processing required for high-resolution inputs, which often introduces disruptive stitching artifacts. To mitigate this issue, we present a unified framework built on three complementary components: 1) Accelerated Virtual-Tiling, which simulates boundary interactions during training to improve spatial consistency without incurring the memory cost of multi-patch encoding; 2) Seam-Targeted Distance-Masked Self-Attention, a latent bottleneck mechanism that enables information exchange across patch boundaries; and 3) Boundary-Aware Regularization, which enforces consistency at tile interfaces through an explicit loss formulation. By explicitly modeling cross-boundary dependencies, the proposed method effectively suppresses stitching artifacts while maintaining scalability to high-resolution inputs. Extensive experiments on the Kodak, JPEG AI, and CLIC 2025 datasets demonstrate competitive or superior rate-distortion performance, achieving high structural fidelity with MS-SSIM values of approximately 0.998 at high compression ratios. These results indicate that the proposed framework provides an effective solution with substantially reduced boundary discontinuities for advanced neural image compression systems based on hybrid CNN-Transformer architectures.