用多模态输入生成高精度3D服装裁片图，速度比现有方法快100倍。
GarmentDiffusion: 3D Garment Sewing Pattern Generation with Multimodal Diffusion Transformers
- 将3D裁片参数转为紧凑边标记，序列长度缩短10倍。
- 采用扩散Transformer并行去噪，生成速度提升100倍。
- 支持文本、图像和不完整裁片输入，适合服装设计自动化。
服装裁片是连接设计概念与实际制造的核心元素。生成式建模对创造多样化服装至关重要，但现有方法或依赖单一输入模态，或生成效率不足。本文提出GarmentDiffusion，一种从多模态输入（文本、图像、不完整裁片）生成厘米级精确、向量化的3D裁片图的新模型。该方法将3D裁片参数高效编码为紧凑的边标记表示，序列长度仅为DressCode中自回归模型SewingGPT的十分之一。通过扩散Transformer，沿时间轴并行去噪所有边标记，且去噪步数恒定，不受数据集边数和面板统计影响。在所有组合设置下，生成速度相比SewingGPT提升100倍。在DressCodeData和最大规模裁片数据集GarmentCodeData上均达到新最优性能。
原文摘要 · Abstract (English)
Garment sewing patterns are fundamental design elements that bridge the gap between design concepts and practical manufacturing. The generative modeling of sewing patterns is crucial for creating diversified garments. However, existing approaches are limited either by reliance on a single input modality or by suboptimal generation efficiency. In this work, we present GarmentDiffusion, a new generative model capable of producing centimeter-precise, vectorized 3D sewing patterns from multimodal inputs (text, image, and incomplete sewing pattern). Our method efficiently encodes 3D sewing pattern parameters into compact edge token representations, achieving a sequence length that is 10 times shorter than that of the autoregressive SewingGPT in DressCode. By employing a diffusion transformer, we simultaneously denoise all edge tokens along the temporal axis, while maintaining a constant number of denoising steps regardless of dataset-specific edge and panel statistics. With all combination of designs of our model, the sewing pattern generation speed is accelerated by 100 times compared to SewingGPT. We achieve new state-of-the-art results on DressCodeData, as well as on the largest sewing pattern dataset, namely GarmentCodeData. The project website is available at https://shenfu-research.github.io/Garment-Diffusion/.
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