发表机构
The Hong Kong University of Science and Technology (Guangzhou); Knowin AI; The Hong Kong University of Science and Technology（香港科技大学（广州）; Knowin AI; 香港科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Seg3DParts提出基于分割的可控部件级三维生成框架，利用显式分割信号定义部件身份，并通过跨部件交互实现连贯组装，在共享空间直接生成对齐网格，配合大规模数据集PartObjectNet，显著提升几何质量与可控性。
AI 中文摘要
部件级三维资产对于编辑、重组和交互至关重要，然而从单张图像恢复此类结构仍具挑战性，原因在于遮挡、模糊边界以及连贯的多部件推理需求。现有方法难以同时实现可控的部件级生成和连贯的多部件结构，因为部件身份和空间分配通常是隐式推断的。我们提出Seg3DParts，一个基于分割的框架，用于从单张图像进行可控的部件级三维生成。通过将分割视为显式的接地信号，我们的方法在生成过程中定义部件身份，使每个组件能够锚定到对应的图像区域。为确保连贯的组装，我们引入了结构化的跨部件交互，允许组件在生成过程中交换全局上下文。因此，Seg3DParts直接在共享的规范空间中生成对齐良好的部件网格，无需事后对齐，支持灵活且可控的分解。我们进一步引入PartObjectNet，一个包含超过20万个对象和100万个标注部件的大规模数据集。实验表明，Seg3DParts在几何质量、跨部件连贯性和部件级可控性方面均优于现有方法。
英文摘要
Part-level 3D assets are essential for editing, reassembly, and interaction, yet recovering such structure from a single image remains challenging due to occlusion, ambiguous boundaries, and the need for coherent multi-part reasoning. Existing approaches struggle to achieve both controllable part-level generation and coherent multi-part structure, as part identity and spatial allocation are typically inferred implicitly. We present Seg3DParts, a segmentation-grounded framework for controllable part-level 3D generation from a single image. By treating segmentation as an explicit grounding signal, our method defines part identity during generation, enabling each component to be anchored to a corresponding image region. To ensure coherent assemblies, we introduce structured cross-part interaction that allows components to exchange global context throughout the generative process. As a result, Seg3DParts directly generates well-aligned part meshes in a shared canonical space without post-hoc alignment, supporting flexible and controllable decomposition. We further introduce PartObjectNet, a large-scale dataset with over 200K objects and 1M annotated parts. Experiments demonstrate that Seg3DParts achieves superior geometry quality, cross-part coherence, and part-level controllability over existing methods.
CommentsAccepted at NeurIPS 2026
