RAGDiffusion++: From Macro-Retrieval to Micro-Fidelity Alignment for Garment Generation
Standard clothing asset generation---restoring forward-facing flat-lay garment images from diverse real-world contexts---holds immense commercial value yet demands both macroscopic topological accuracy and microscopic physical fidelity. Although our previous work RAGDiffusion effectively eradicated large-scale structural hallucinations via retrieval-augmented macro-constraints, achieving industrial-grade micro-texture realism remains an unsolved bottleneck. We formally identify this limitation as High-Frequency Trajectory Collapse: supervised fine-tuning (SFT) converges to the conditional mean of the training distribution, which is dominated by smooth, low-frequency textures, causing high...
| Authors | Yuhan Li, Xianfeng Tan, Fangao Zeng et al. |
|---|---|
| Primary category | cs.CV |
| Published | 29 August 2026 |
| Last updated at source | 29 August 2026 |
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