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FitDiT: Advancing the Authentic Garment Details for High-fidelity Virtual Try-on
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FitDiT: Advancing the Authentic Garment Details for High-fidelity Virtual Try-on
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Although image-based virtual try-on has made considerable progress, emerging approaches still encounter challenges in producing high-fidelity and robust fitting images across diverse scenarios. These methods often struggle with issues such as texture-aware maintenance and size-aware fitting, which hinder their overall effectiveness. To address these limitations, we propose a novel garment perception enhancement technique, termed FitDiT, designed for high-fidelity virtual try-on using Diffusion Transformers (DiT) allocating more parameters and attention to high-resolution features. First, to further improve texture-aware maintenance, we introduce a garment texture extractor that incorporates garment priors evolution to fine-tune garment feature, facilitating to better capture rich details such as stripes, patterns, and text. Additionally, we introduce frequency-domain learning by customizing a frequency distance loss to enhance high-frequency garment details. To tackle the size-aware fitting issue, we employ a dilated-relaxed mask strategy that adapts to the correct length of garments, preventing the generation of garments that fill the entire mask area during cross-category try-on. Equipped with the above design, FitDiT surpasses all baselines in both qualitative and quantitative evaluations. It excels in producing well-fitting garments with photorealistic and intricate details, while also achieving competitive inference times of 4.57 seconds for a single 1024x768 image after DiT structure slimming, outperforming existing methods.
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Cited by 12 Pith papers
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A two-stage diffusion pipeline and new benchmark let virtual try-on add, remove, or swap clothing layers while preserving inner layers, with SOTA results on the new LVTON benchmark and on VITON-HD/DressCode.
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CtrlVTON: Controllable Virtual Try-On via Visual-Instance-Prompt Segmentation
CtrlVTON recasts virtual try-on as mask-conditioned editing and introduces VIP-SAM for instance-level garment segmentation, beating proprietary editors on layout fidelity while matching garment quality.
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OrthoTryOn: Geometric Orthogonalization for Conflict-Free Unified Fashion Generation
OrthoTryOn uses Orthogonal Subspace Projection on shared LoRA and Fisher-guided Negative Guidance to enable conflict-free unified fashion generation, outperforming task-specific models on benchmarks.
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DirectTryOn: One-Step Virtual Try-On via Straightened Conditional Transport
DirectTryOn achieves state-of-the-art one-step virtual try-on performance by applying pure conditional transport, garment preservation loss, and self-consistency loss to straighten trajectories in pretrained generativ...
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TripVVT: A Large-Scale Triplet Dataset and a Coarse-Mask Baseline for In-the-Wild Video Virtual Try-On
A new large-scale triplet dataset and diffusion transformer model using coarse human masks deliver improved video virtual try-on quality and generalization in challenging real-world conditions.
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Structure-Detail Decoupled Autoregressive Generation for Fast and High-Fidelity Virtual Try-On
STAR-VTON decouples latent VAR structure synthesis from pixel-space matching-based detail recovery, yielding faster high-fidelity virtual try-on than diffusion baselines.
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Oxygen-TryOn: Fashion-Native Foundation Model for Any-item Virtual Try-On
Oxygen-TryOn performs any-item, multi-reference virtual try-on via understanding-driven generation, reporting state-of-the-art scores on public and internal benchmarks.
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The devil is in the details: Enhancing Video Virtual Try-On via Keyframe-Driven Details Injection
KeyTailor improves video virtual try-on realism by using instruction-guided keyframes to enhance garment details and background integrity in DiT models without major architectural changes.
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TAMF-VTON: Texture-Aware Mask-Free Virtual Try-On via High-Fidelity Image Synthesis
TAMF-VTON is a mask-free diffusion virtual try-on system using Mixture-of-Experts adapters and frequency-domain supervision, reporting SOTA results on VITON-HD and DressCode with multi-garment support.
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FitVTON: Fit-aware Virtual Try-On via Body-Garment Size Control
FitVTON introduces a fit-aware virtual try-on model using text prompts for size control, auxiliary garment/body mask prediction, and texture rectification to achieve better sizing accuracy on diverse bodies than prior...
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Tstars-Tryon 1.0: Robust and Realistic Virtual Try-On for Diverse Fashion Items
Tstars-Tryon 1.0 is a deployed virtual try-on system claiming high robustness, photorealism, multi-reference flexibility, and near real-time speed for diverse fashion items.
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Tstars-Tryon 1.0: Robust and Realistic Virtual Try-On for Diverse Fashion Items
Tstars-Tryon 1.0 is a robust, photorealistic virtual try-on system with multi-image support and near real-time speed, deployed at industrial scale on Taobao and accompanied by a released benchmark.
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