End-to-End 3D Part Generation

• The paper’s main contribution is the unified generation of semantically segmented, editable 3D parts using compositional latent spaces and hierarchical attention.
• It emphasizes efficient synthesis through parallel decoding and dual volume packing, reducing computation time while ensuring coherent part boundaries.
• The framework enables practical applications in graphics, robotics, and simulation by facilitating direct asset editing and precise, part-level control.

An end-to-end framework for part-level 3D object generation refers to a class of generative models and systems that synthesize 3D shapes explicitly as assemblies of semantically meaningful, geometrically distinct parts. Unlike monolithic 3D generation, these frameworks output decomposable, editable components, facilitating applications in content creation, animation, robotics, and 3D understanding. Recent advances have shifted from two-stage pipelines—segmenting and reconstructing parts separately—toward unified models that generate all parts and their spatial relations simultaneously, often from high-level conditioning (e.g., images, text, or scene graphs).

1. Fundamental Problem and Motivation

Part-level 3D generation addresses the synthesis of 3D assets composed of multiple, separable parts. This granular control is vital for editing, texture assignment, physical simulation, rigging, and manipulating assets in downstream tasks. Typical challenges include:

• Handling variable part counts and configurations across objects.
• Ensuring global structural coherence while preserving per-part semantic and geometric independence.
• Generating plausible shapes and part boundaries even with occluded or ambiguous input.
• Enabling efficient, scalable synthesis without reliance on post-hoc mesh segmentation.

The field has moved toward end-to-end systems capable of generating full objects decomposed into parts—each as a distinct, watertight 3D mesh or volumetric region—and often from a single image, textual description, or scene-level specification (2506.05573, 2407.13677, 2412.18608).

2. Representative Model Architectures

Recent frameworks share some common architectural elements, while introducing novel mechanisms to address the unique challenges of part-level generation.

2.1 Compositional Latent Spaces

Models such as PartCrafter (2506.05573) and TAR3D (2412.16919) employ compositional or sequential latent representations:

• For each part, a dedicated latent code or token set is maintained (e.g., $\boldsymbol{z}_i$ for part $i$), allowing modularity and direct correspondence between latent space and part structure.
• These latent codes are collectively processed either in parallel (as sets) or autoregressively (as sequences) depending on the framework.
• Permutation invariance or hierarchical organization is introduced to support objects with variable numbers of parts ((2407.13677) uses set-based attention; (2412.07237) employs tree-structured tokenization for articulation).

2.2 Conditional Generation and Input Modalities

Frameworks accommodate varied conditioning—including single RGB images, text, scene graphs, or multi-view images:

• PartCrafter: Accepts single RGB images, extracting features with DINOv2 as global conditioning.
• PASTA (2407.13677), ArtFormer (2412.07237): Accept partial part sets, text, image embeddings, or bounding boxes as cues, allowing for shape completion and multimodal control.
• Vote2Cap-DETR++ (2309.02999): Jointly localizes parts/objects in point clouds and describes them, decoupling spatial and semantic reasoning.
• PartGen (2412.18608): Uses text, images, or unstructured 3D for multi-view generation, segmentation, and per-part completion via diffusion models.

2.3 Hierarchical Attention and Structured Information Flow

To maintain both intra-part detail and inter-part/global cohesion:

• Hierarchical Attention: Local (within-part) and global (across-part) attention blocks alternate in the backbone (e.g., DiT-based (2506.05573)), ensuring detail preservation and structural coordination.
• Part Identity Embeddings: Models introduce explicit or learned part ID embeddings to maintain consistent mapping and permutation-invariance.

2.4 Generation and Decoding

Decoding strategies vary:

• Mesh-based Generative Transformers: Synthesize explicit mesh representations for each part, suitable for rendering and direct editing (2506.05573).
• Autoregressive Transformers: Use next-part prediction and attribute-wise sequential modeling (as in PASTA (2407.13677), TAR3D (2412.16919)).
• Diffusion Models: Denoise the latent codes for all parts jointly (rectified flow (2506.05573)) or per-part via diffusion processes ((2412.18608), for multi-view image synthesis and 3D reconstruction).
• Volume/SDF Decoding: Parts are decoded using signed distance functions or neural occupancy fields (2412.16919), supporting smooth, watertight mesh reconstructions with triplane or volumetric priors.

3. Innovations and Solutions for Part-Level Generation

3.1 Dual Volume Packing (2506.09980)

To efficiently handle arbitrary numbers of non-contacting, editable parts (avoiding fused components in SDF volumes):

• Parts are assigned to two non-overlapping groups (volumes) according to the object’s connectivity graph (modeled as a graph coloring problem).
• An algorithm contracts cycles in the graph (via merging most-contacting pairs) to achieve bipartition, ensuring that each extracted volume contains only disjoint (non-contacting) parts.
• Parts are then generated via parallel decoding and combined at mesh extraction.

This enables constant-time generation even as the part count grows, in contrast to sequential part-wise processing.

3.2 Hierarchical Decomposition and End-to-End Synthesis

Frameworks like PartCrafter (2506.05573) and ArtFormer (2412.07237) support:

• One-shot, end-to-end synthesis of all desired parts, either simultaneously (set-based) or in an autoregressive, tree-structured sequence (for articulated and hierarchical models).
• Part-aware compositional latent spaces, supporting modular editing, addition/removal of parts, and variable semantic granularity.
• The possibility to reconstruct occluded or hypothetical parts using learned priors, overcoming missing data.

4. Empirical Evaluation and Comparison

4.1 Quantitative Results

Comprehensive experiments measure metrics pertinent to both overall geometry and per-part decomposition:

Method	Chamfer Dist. (CD) ↓	F-Score ↑	IoU (parts) ↓	Time
HoloPart	0.1916	0.6916	0.0443	18 min
TripoSG	0.1821	0.7115	—	—
PartCrafter	0.1726	0.7472	0.0359	34 s

• Lower Chamfer distance and higher F-score indicate better geometric accuracy and reconstructive fidelity.
• Lower part-level IoU demonstrates more distinct, non-overlapping decomposition.

4.2 Comparisons with Prior Art

• Two-stage approaches (segmentation + per-part generation, e.g., HoloPart, MIDI) are significantly slower and rely on segmentation accuracy, often yielding inferior separation and error-prone propagation.
• PartCrafter and similar unified models generate decomposable, editable meshes in significantly less time, outperforming baselines without requiring pre-segmented input (2506.05573, 2506.09980).
• Dual Volume Packing (2506.09980) further accelerates inference by mapping the part assignment problem to a fixed number of volumes, supporting efficient batch generation.

5. Downstream Applications and Broader Implications

• Editable Asset Creation: Modular meshes with explicit part boundaries enable direct editing, rigging, animation, and physical simulation for games, AR/VR, and digital twins.
• Semantically Aware Texturing: Distinct parts allow for context-driven or user-selected texture assignment, improving realism and user control.
• Robotics and Simulation: Fine-grained, editable part structures facilitate manipulation, grasp planning, and reverse engineering, with each part mapped to physical or functional roles.
• 3D Understanding and Annotation: End-to-end part generation supports new benchmarks and fine-grained annotation datasets, aiding research in scene understanding and object-centric learning.

This suggests a foundational shift in 3D generative modeling: the move from monolithic, holistic representations to structured, compositional frameworks, reflecting both object semantics and geometry, and closing the gap between generation and downstream manipulation needs.

6. Limitations and Prospects for Further Research

Identified bottlenecks and open research directions:

• Granularity Control: Current approaches lack explicit mechanisms for user-specified or prompt-driven part granularity. Integrating interactive controls or text-based specifications would increase flexibility (2506.09980).
• Beyond Two-Volume Packing: Some complex objects exhibit connectivity graphs that cannot be properly partitioned with only two volumes. Extending to multi-volume packing, inspired by graph coloring theory, is an active area (2506.09980).
• Annotation Consistency: The frameworks depend on part-labeled datasets; inconsistencies in annotations may reduce the stability and interpretability of part predictions. More robust annotation schemes or semi-supervised learning of part boundaries are needed.
• Handling Topological Complexities: Highly entangled structures (e.g., nested, intertwined parts) and weakly-defined semantic boundaries remain challenging, both in packing and in generative separation.

A plausible implication is that future systems will incorporate prompt-conditioned part splitting, richer hierarchical decomposition, and more flexible, user-in-the-loop control of part assignments, further bridging practical 3D generation and real-world application requirements.

7. Table: Framework Innovations at a Glance

Framework	Part Representation	Generation Mode	Key Innovation
PartCrafter (2506.05573)	Sets of latent tokens	Parallel (one-shot)	Compositional latent space, hierarchical attention
TAR3D (2412.16919)	Triplane/VQ codebooks	Autoregressive (next-part)	Next-part prediction in triplane codebook space
PASTA (2407.13677)	Cuboidal primitives	Autoregressive (parts)	Unordered, interpretable part sequences
Dual Volume Packing (2506.09980)	SDF Volumes	Parallel (dual volumes)	Bipartite packing of parts, fixed runtime
ArtFormer (2412.07237)	Tree-structured tokens	Autoregressive (tree)	Articulation graph via transformer, SDF prior
PartGen (2412.18608)	Multi-view diffused parts	Multi-view/diffusion	Generative per-part segmentation & completion

References to Core Works

• PartCrafter: (2506.05573)
• Dual Volume Packing: (2506.09980)
• PASTA: (2407.13677)
• ArtFormer: (2412.07237)
• TAR3D: (2412.16919)
• PartGen: (2412.18608)

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The emergence of end-to-end part-level 3D object generation frameworks reflects a convergence of compositional, permutation-invariant latent spaces, hierarchical and context-aware attention, innovative decoding strategies (mesh, volume, sequence), and efficiency-driven architectural choices. This enables precise, semantic, and editable 3D asset synthesis aligned with the requirements of modern applications in graphics, robotics, simulation, and interactive design.