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X-Part: high fidelity and structure coherent shape decomposition
Xinhao Yan, Jiachen Xu, Yang Li, Changfeng Ma, Yunhan Yang, Chunshi Wang, Zibo Zhao, Zeqiang Lai, Yunfei Zhao, Zhuo Chen, Chunchao Guo
TL;DR
Existing part-based 3D generation methods provide limited controllability and may fail to produce semantically meaningful decompositions, despite the importance of parts for downstream tasks. X-Part addresses this with bounding-box prompts, semantic point-feature injection, synchronized diffusion, and interactive editing, achieving state-of-the-art part-level decomposition and generation performance. The method also supports downstream workflows such as UV unwrapping while retaining editable part control.
Problem
Existing approaches struggle to decompose complete 3D shapes into semantically meaningful parts while recovering geometrically plausible structures for downstream tasks.
Method
X-Part uses bounding-box prompts, point-wise semantic features, synchronized multi-part diffusion, and a training-free editing pipeline for controllable part generation.
Results
X-Part achieves state-of-the-art performance in part-level decomposition and generation across extensive experiments.
Takeaways & Limitations
X-Part supports interactive part editing and can simplify UV unwrapping by producing compact, semantically meaningful part-based UV maps.
Takeaways & Limitations
The method relies on geometric cues without physical-principle guidance, and simultaneous processing makes inference slower as the number of parts increases.
Abstract
from arXiv · showhide
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.
1 Tencent Hunyuan, 2ShanghaiTech, 3NJU, 4HKU, 5ZJU, 6CUHK
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1 Introduction
X-Part addresses the difficulty of generating semantically meaningful and geometrically plausible 3D parts for practical creation pipelines. It uses bounding-box prompts, semantic point features, and synchronized diffusion to produce controllable part decompositions, achieving state-of-the-art performance in reported experiments.
- Existing generative approaches mainly produce monolithic models, while part generation must preserve semantic meaning and plausible internal geometry.
- X-Part uses P3-SAM to generate initial segmentations, bounding boxes, and semantic features before synchronized multi-part diffusion.
- Bounding boxes provide coarse location and scale prompts, while point-wise semantic features supply more robust semantic representations.
- X-Part achieves state-of-the-art performance in part-level decomposition and generation across extensive experiments.
- The framework includes an editable part-generation pipeline supporting multiple interactive editing methods.
2 Related Work
Prior work spans supervised and foundation-model-based segmentation, object-level 3D generation, and emerging part-level generation methods. X-Part is presented within this landscape as a diffusion architecture for guided decomposition.
- Earlier part segmentation methods rely on extensive annotations, generalize poorly beyond seen categories, and have limited semantic scalability.
- Object-level 3D generation methods extend diffusion-based image-generation ideas but can suffer from slow optimization and geometric inconsistency.
- Part-level generation methods include multi-view segmentation, physical regularization, and DiT-based generative approaches.
- X-Part’s architecture extracts point features and part bounding boxes, forms global and part conditions, and injects them into multi-part diffusion.
3 Method
X-Part uses object and part-level conditions in a synchronized latent diffusion framework to generate semantically meaningful, structurally coherent 3D parts. Bounding-box cues, semantic features, inter-part attention, and an editing pipeline provide controllable generation and manipulation.
- 3.2.1 Semantic-Aware Shape Conditioning: Bounding-box prompts specify part locations and scales, while perturbation augmentation improves robustness during inference.Points within each box are encoded into the part-level condition, and random translations and scale changes are applied during training.
- 3.2.1 Semantic-Aware Shape Conditioning: Point-wise P3-SAM semantic features are concatenated with VAE shape tokens to improve decomposition accuracy despite segmentation inaccuracies.Features are interpolated using down-sampled XYZ positions and randomly masked to improve robustness to high-dimensional semantic inputs.
- 3.2 Framework: X-Part encodes object and part conditions, then simultaneously generates latent tokens for all parts in a synchronized diffusion process.The object condition comes from the VAE encoder, while part conditions are extracted from points inside bounding boxes and enhanced with semantic features.
- 3.2.2 Synchronized Part Generation: Inter-part attention extends receptive fields across part tokens, improving boundary awareness and structural consistency while cross-attention preserves input geometry.Half of the self-attention layers attend across all parts; cross-attention injects geometric conditions into the generation process.
- 3.2.2 Synchronized Part Generation: A flow-matching objective trains the DiT network to transport noisy part tokens toward target latent tokens, using fewer tokens per part to accelerate training and inference.The model predicts a velocity field conditioned on object- and part-level features, and reduced per-part token counts exploit lower individual-part complexity.
- 3.3 Part Editing: The training-free editing pipeline supports split and adjust operations by resampling and denoising only the latent tokens associated with edited bounding boxes.Tokens for unchanged parts remain fixed during interactive generation.
4 Experiments
X-Part is evaluated against existing decomposition and image-to-3D part-generation methods using geometric metrics, qualitative comparisons, applications, and ablations. Results indicate higher geometric quality, refined semantic decomposition, interactive editing, improved UV unwrapping, and contributions from the framework’s components.
- Implementation Details: Training uses nearly 2.3 million objects with ground-truth part segmentation, remeshed into watertight object and part meshes.The large-scale dataset is reported to improve the generalizability of the diffusion-based decomposition method.
- Comparison: The evaluation uses 200 ObjaversePart-Tiny samples with Chamfer Distance and F-Score at thresholds 0.1 and 0.5.Objects are normalized before metric computation and evaluated in a pose-agnostic setting.
- Comparison: X-Part achieves higher geometric quality and better alignment to ground truth, often producing more numerous semantically reasonable parts.The comparison evaluates both overall object geometry and decomposed parts against existing methods.
- Applications: Bounding-box edits control part shape and coverage, while merging or splitting boxes enables fusion or finer decomposition.Users can adjust bounding-box position and scale to influence generated part geometry.
- Applications: Part decomposition simplifies UV unwrapping and produces more compact, semantically meaningful UV maps than holistic-mesh unwrapping.The comparison unwraps holistic meshes versus each separated part independently.
- Ablation Study: Ablations attribute performance improvements to attention mechanisms, part embeddings, object- and part-level conditions, and semantic point features.The components respectively support contextual part representations, part distinctiveness, geometric priors, location and scale information, and coherent decomposition.
5 Conclusion and Limitation
X-Part decomposes complete 3D objects into structured parts using bounding-box controls and semantic point features, supporting editable generation and downstream content-creation workflows. Its main limitations are the lack of physical-principle guidance and increasing inference time as part count grows.
- Conclusion: X-Part is a geometry-based framework that uses bounding boxes to generate structured parts from complete 3D objects.Semantic point features improve structural understanding, while users can alter decomposition through bounding-box adjustments.
- Conclusion: The editable pipeline supports intuitive decomposition control and can generate up to 50 distinct parts for practical applications.The method is intended to reduce downstream 3D content-creation complexity.
- Limitation: Decomposition lacks physical-principle guidance, which may limit applications requiring specific physically informed decompositions.This is identified as a current limitation of the geometry-cue-based approach.
- Limitation: Inference time increases with the number of simultaneously processed parts, challenging real-time use for high-part-count objects.All part latent codes are processed together through the diffusion model.