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IMAGGarment: Fine-Grained Garment Generation for Controllable Fashion Design

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arxiv 2504.13176 v2 pith:6CUREVJI submitted 2025-04-17 cs.CV

Fei Shen , Jian Yu , Cong Wang , Xin Jiang , Xiaoyu Du , Jinhui Tang This is my paper
classification cs.CV
keywords colorimaggarmentgarmentdesigngenerationlocalmodelappearance
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper presents IMAGGarment, a fine-grained garment generation (FGG) framework that enables high-fidelity garment synthesis with precise control over silhouette, color, and logo placement. Unlike existing methods that are limited to single-condition inputs, IMAGGarment addresses the challenges of multi-conditional controllability in personalized fashion design and digital apparel applications. Specifically, IMAGGarment employs a two-stage training strategy to separately model global appearance and local details, while enabling unified and controllable generation through end-to-end inference. In the first stage, we propose a global appearance model that jointly encodes silhouette and color using a mixed attention module and a color adapter. In the second stage, we present a local enhancement model with an adaptive appearance-aware module to inject user-defined logos and spatial constraints, enabling accurate placement and visual consistency. To support this task, we release GarmentBench, a large-scale dataset comprising over 180K garment samples paired with multi-level design conditions, including sketches, color references, logo placements, and textual prompts. Extensive experiments demonstrate that our method outperforms existing baselines, achieving superior structural stability, color fidelity, and local controllability performance. Code, models, and datasets are publicly available at https://github.com/muzishen/IMAGGarment.

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Cited by 16 Pith papers

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