用文本、身材和草图控制，生成复杂且贴身的服装版型。
Multimodal Latent Diffusion Model for Complex Sewing Pattern Generation
- 将版型向量化为紧凑潜在空间，支持细节丰富建模。
- 多模态条件联合训练，实现文字/体型/草图精准控制。
- 适合需要高度定制化设计的服装设计师或工业应用。
由于其对计算机图形友好且可灵活编辑，服装版型生成日益受到关注。现有方法虽能生成精美服装，但在设计复杂款式时缺乏细节控制。为此，我们提出SewingLDM，一种多模态生成模型，可通过文本提示、身体形状和服装草图生成服装版型。首先，我们将原始版型向量扩展为更全面的表示以涵盖更多细节，并压缩至紧凑的潜在空间。为学习该潜在空间中的版型分布，设计了两阶段训练策略，将身体形状、文本提示和服装草图等多模态条件注入扩散模型，确保生成服装贴合体型且细节可控。大量定性和定量实验表明，该方法在复杂服装设计与多种体型适应性方面显著优于先前方法。
原文摘要 · Abstract (English)
Generating sewing patterns in garment design is receiving increasing attention due to its CG-friendly and flexible-editing nature. Previous sewing pattern generation methods have been able to produce exquisite clothing, but struggle to design complex garments with detailed control. To address these issues, we propose SewingLDM, a multi-modal generative model that generates sewing patterns controlled by text prompts, body shapes, and garment sketches. Initially, we extend the original vector of sewing patterns into a more comprehensive representation to cover more intricate details and then compress them into a compact latent space. To learn the sewing pattern distribution in the latent space, we design a two-step training strategy to inject the multi-modal conditions, \ie, body shapes, text prompts, and garment sketches, into a diffusion model, ensuring the generated garments are body-suited and detail-controlled. Comprehensive qualitative and quantitative experiments show the effectiveness of our proposed method, significantly surpassing previous approaches in terms of complex garment design and various body adaptability. Our project page: https://shengqiliu1.github.io/SewingLDM.
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