Practical garment design spans two modes: intuitive creation from high-level intent, such as a reference image or text description, and complex low-level editing across 2D sewing patterns and 3D draped geometry, which requires professional training to navigate their complex interdependencies. Yet existing frameworks address only part of this challenge, offering either garment generation from casual inputs or direct editing on sewing patterns. To support both ends of the spectrum, we propose Garment Particles, a 5D point-cloud representation that jointly encodes 2D sewing patterns and 3D geometry. This representation enables Garment Particles Flow (GPF), a rectified flow framework that supports intuitive generation from high-level inputs (text, images, sketches) and various editing operations on 2D sewing patterns and 3D geometries via diffusion posterior sampling. Finally, we introduce Particles-to-Pattern Flow that converts generated garment particles into curved-based patterns for simulation. We validate our model's generation ability on multiple datasets, achieving state-of-the-art garment generation results against competitive baselines. Our model also enables many garment editing scenarios, including garment interpolation, sewing pattern editing, point-cloud- and silhouette-conditioned garment generation. Our project website is at https://garment-particles.github.io .

翻译：摘要：实用服装设计涵盖两种模式：基于高层意图（如参考图像或文本描述）的直观创作，以及涉及2D纸样与3D悬垂几何的复杂低层编辑——后者需要专业训练才能驾驭其复杂的相互依赖关系。然而现有框架仅解决该挑战的部分问题，要么支持基于随意输入的服装生成，要么支持纸样上的直接编辑。为兼顾光谱两端的需求，我们提出服装粒子（Garment Particles），一种联合编码2D纸样与3D几何的五维点云表示。该表示支撑了服装粒子流（GPF），一种整流流框架，通过扩散后验采样支持基于高层输入（文本、图像、草图）的直观生成，以及针对2D纸样与3D几何的多种编辑操作。最后，我们引入粒子-纸样流（Particles-to-Pattern Flow），将生成的服装粒子转换为基于曲线的纸样以支持仿真。我们在多个数据集上验证了模型的生成能力，相较于竞争基线实现了最优的服装生成结果。本模型同时支持多种服装编辑场景，包括服装插值、纸样编辑、点云约束及轮廓约束的服装生成。项目网站见https://garment-particles.github.io。