X-Part: high fidelity and structure coherent shape decomposition
This addresses the need for production-ready, editable 3D assets in fields such as computer graphics and manufacturing, representing a new paradigm rather than an incremental improvement.
The paper tackles the problem of generating 3D shapes at the part level, which is crucial for applications like mesh retopology and 3D printing, by introducing X-Part, a controllable generative model that achieves state-of-the-art performance in decomposing holistic 3D objects into semantically meaningful and structurally coherent parts with high geometric fidelity.
Generating 3D shapes at part level is pivotal for downstream applications such as mesh retopology, UV mapping, and 3D printing. However, existing part-based generation methods often lack sufficient controllability and suffer from poor semantically meaningful decomposition. To this end, we introduce X-Part, a controllable generative model designed to decompose a holistic 3D object into semantically meaningful and structurally coherent parts with high geometric fidelity. X-Part exploits the bounding box as prompts for the part generation and injects point-wise semantic features for meaningful decomposition. Furthermore, we design an editable pipeline for interactive part generation. Extensive experimental results show that X-Part achieves state-of-the-art performance in part-level shape generation. This work establishes a new paradigm for creating production-ready, editable, and structurally sound 3D assets. Codes will be released for public research.