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.
TAMF-VTON is presented, a texture-aware, mask-free framework that enables high-fidelity image synthesis under practical unconstrained conditions and outperforms state-of-the-art methods in both quantitative metrics and perceptual quality.
This work proposes RIG (Regional Image-prompt Generation), a novel training-free framework for multi-reference personalized generation that 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...
This work proposes TurboClear, a one-step SDXL-based object-effect removal model, and designs Region-Calibrated Distribution Matching (RDM) for region-aware distillation to preserve the teacher model's asymmetric edit-and-preserve behavior.
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.
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