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：实际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，以及需经专业训练方可驾驭的复杂底层编辑（涉及2D纸样与3D悬垂几何的相互依赖关系）。然而现有框架仅解决部分挑战，或支持从非正式输入生成服装，或直接在纸样上进行编辑。为同时支撑这两个极端场景，我们提出服装粒子（Garment Particles）——一种联合编码2D纸样与3D几何的点云表示。该表示使能服装粒子流（GPF），通过扩散后验采样支持从高层输入（文本、图像、草图）的直观生成，以及2D纸样与3D几何上的多种编辑操作。最后，我们提出粒子-纸样流（Particles-to-Pattern Flow），将生成的服装粒子转化为曲线纸样以进行仿真。我们在多个数据集上验证了模型生成能力，相较竞争基准取得了最先进的服装生成结果。此外，本模型还支持多种服装编辑场景，包括服装插值、纸样编辑、点云与轮廓条件生成。项目网站见https://garment-particles.github.io。