Title: PartCrafter: Structured 3D Mesh Generation via Compositional Latent Diffusion Transformers
Venue: NeurIPS 2025
Accepted: yes
Keywords: 3D Generation

3D Part-level Object Generation

3D Scene Generation

Generative Models
Abstract: We introduce PartCrafter, the first structured 3D generative model that jointly synthesizes multiple semantically meaningful and geometrically distinct 3D meshes from a single RGB image. Unlike existing methods that either produce monolithic 3D shapes or follow two-stage pipelines, i.e. first segmenting an image and then reconstructing each segment, PartCrafter adopts a unified, compositional generation architecture that does not rely on pre-segmented inputs. Conditioned on a single image, it simultaneously denoises multiple 3D parts, enabling end-to-end part-aware generation of both individual objects and complex multi-object scenes. PartCrafter builds upon a pretrained 3D mesh diffusion transformer (DiT) trained on whole objects, inheriting the pretrained weights, encoder, and decoder, and introduces two key innovations: (1) A compositional latent space, where each 3D part is represented by a set of disentangled latent tokens; (2) A hierarchical attention mechanism that enables structured information flow both within individual parts and across all parts, ensuring global coherence while preserving part-level detail during generation. To support part-level supervision, we curate a new dataset by mining part-level annotations from large-scale 3D object datasets. Experiments show that PartCrafter outperforms existing approaches in generating decomposable 3D meshes, including parts that are not directly visible in input images, demonstrating the strength of part-aware generative priors for 3D understanding and synthesis. Code and training data are released.

PartCrafter: Structured 3D Mesh Generation via Compositional Latent Diffusion Transformers

Abstract

We introduce PARTCRAFTER, the first structured 3D generative model that jointly synthesizes multiple semantically meaningful and geometrically distinct 3D meshes from a single RGB image. Unlike existing methods that either produce monolithic 3D shapes or follow two-stage pipelines, i.e. first segmenting an image and then reconstructing each segment, PARTCRAFTER adopts a unified, compositional generation architecture that does not rely on pre-segmented inputs. Conditioned on a single image, it simultaneously denoises multiple 3D parts, enabling end-toend part-aware generation of both individual objects and complex multi-object scenes. PARTCRAFTER builds upon a pretrained 3D mesh diffusion transformer (DiT) trained on whole objects, inheriting the pretrained weights, encoder, and decoder, and introduces two key innovations: (1) A compositional latent space , where each 3D part is represented by a set of disentangled latent tokens; (2) A hierarchical attention mechanism that enables structured information flow both within individual parts and across all parts, ensuring global coherence while preserving part-level detail during generation. To support part-level supervision, we curate a new dataset by mining part-level annotations from large-scale 3D object datasets. Experiments show that PARTCRAFTER outperforms existing approaches in generating decomposable 3D meshes, including parts that are not directly visible in input images, demonstrating the strength of part-aware generative priors for 3D understanding and synthesis. Code and training data are released.

3

1 Introduction

A central organizing principle in perception is the role of objects and parts-semantically coherent units that serve as compositional building blocks for higher-level cognitive tasks, including language, planning, and reasoning. This part-based structure facilitates generalization, as parts can be independently interpreted, recombined, and reused across different contexts. In contrast, most contemporary neural networks lack the ability to form and manipulate such structured, symbol-like entities.

In 3D generation, diffusion-based generative models have shown strong capabilities in synthesizing entire 3D object meshes from scratch or from images [1, 2, 3]. However, these models typically operate at the whole-object level and do not support part-level decomposition. This limitation restricts their applicability in downstream tasks such as texture mapping, animation, physical simulation, and scene editing. Some recent efforts have attempted to address this by first segmenting images into semantic parts and then reconstructing each part in 3D [4, 5, 6, 7]. However, this two-stage pipeline suffers from errors in segmentation, extensive computational costs for extra segmentation models, and difficulties in scaling up, thereby limiting both robustness and fidelity.

We introduce PARTCRAFTER, a structured generative model for 3D scenes that enables part-level generation from a single RGB image through a compositional latent space. PARTCRAFTER jointly generates multiple distinct 3D parts by binding each to a dedicated set of latent variables. This disentanglement allows parts to be independently edited, removed, or added without disrupting the rest of the scene. PARTCRAFTER builds upon large-scale pretrained 3D object latent diffusion models, which represent object meshes as sets of latent tokens [8] aligned either explicitly [3] or implicitly [1, 2] to regions in 3D space. PARTCRAFTER restructures these pretrained models into a compositional architecture equipped with a varying number of latent token sets. It guides each latent token set during the denoising process to associate with a particular 3D part entity, through a novel local-global attention mechanism that facilitates both intra-part and inter-part information flow, in an identity-aware and permutation-invariant way. A shared decoder then maps each latent set into a coherent 3D mesh. We initialize the model's encoder, decoder, and denoising transformer using weights pretrained on whole-object mesh generation tasks [1]. When conditioned on a single RGB image, the model produces structured 3D outputs with coherent part-level decomposition, eliminating the need for brittle segmentation-then-reconstruction pipelines.

To support training, we construct a large-scale dataset by mining part-level annotations from existing 3D object repositories. Many assets in datasets like Objaverse [9] contain part information in their GLTF metadata, as they are often authored using modular components. Rather than flattening these into single meshes, as done in prior work, we retain their part annotations. Our curated dataset merges Objaverse [9], ShapeNet [10], and the Amazon Berkeley Objects (ABO) dataset [11], resulting in a rich collection of part-annotated 3D models suitable for learning compositional generation. As for scene-level generation, we leverage the existing 3D scene dataset 3D-Front [12] for training.

We evaluate PARTCRAFTER on both 3D part-level object generation and 3D scene reconstruction, and compare it against existing two-stage methods [6, 7] that first segment the input image and then reconstruct each segment. Our results show that PARTCRAFTER achieves higher generation quality and better efficiency. As shown in our experimental section, PARTCRAFTER can automatically infer invisible 3D structures from a single image. It can be equally well used at the object or scene level, which makes it a universal model for 3D scene reconstruction. Notably, PARTCRAFTER surpasses its underlying 3D object generative model on object reconstruction fidelity, showing that understanding the compositional structure of objects enhances the quality of 3D generation.

In summary, our contributions are as follows:

We propose PARTCRAFTER, a structured 3D generation model with explicit part-level binding, capable of generating semantically meaningful 3D components to compose an object or a scene from image prompts, without any segmentation input.

We design a novel compositional DiT architecture with structured latent spaces, identityaware part-global attention, and shared decoders.

A new dataset with part annotations is curated from existing 3D assets.

PARTCRAFTER achieves state-of-the-art performance on structured 3D generation tasks, demonstrating strong results on both individual objects and complex multi-object scenes. Comprehensive ablation studies validate the contribution of each component in our approach.

2 Related Work

3D Object Generation Previous works on 3D object generation have adopted various representations, including voxels [13, 14, 15], point clouds [16, 17, 18], signed distance fields (SDFs) [19, 20], neural radiance fields (NeRFs) [21, 22, 23, 24, 25], and 3D Gaussian splitting (3DGS) [26, 27, 28]. Wefocus on 3D meshes for their compatibility with real-world 3D content creation pipelines. One line of mesh generation works produces explicit mesh structures autoregressively [29, 30, 31, 32, 33, 34]. The other line of 3D mesh generation methods uses latent diffusion models [3, 8, 35, 36, 37], and has demonstrated strong performance, particularly when training high-capacity diffusion models across large-scale datasets [1, 2, 38, 39, 40]. PARTCRAFTER builds upon the latter line of 3D mesh generative models. However, these methods typically generate 3D objects as holistic entities, neglecting the natural part-based decomposition that exists in 3D object datasets and is commonly employed by human artists during creation. PARTCRAFTER addresses this limitation.

3D Part-level Object Generation 3D part-level object generation is a long-standing problem in computer vision. One line of work [41, 42] has focused on assembling existing parts by inferring the right translations and orientations. Another line of work, which is more related to our work, generates 3D geometry of parts and their structure to form a complete object. Different primitives have been used as representation, such as point clouds [43], voxels [44], implicit neural fields [45, 46, 47, 48], and other parametric representations [49, 50, 51]. These works are limited by either the scale of datasets [10, 52] or the expressiveness of the representation. To improve the visual quality and utilize prior knowledge of 2D pretrained models, several recent works [4, 5] propose to leverage multi-view diffusion and 2D segmentation models [53] to generate NeRF [21] or NeuS [54] with part labels. Concurrent work HoloPart [6] proposes to first segment the 3D object mesh into parts and then refine the parts' geometry with a pretrained 3D DiT [1]. Although effective, these methods are heavily dependent on the segmentation quality and are difficult to scale up. In contrast, our method can generate structured 3D part-level objects in one shot without any 2D or 3D segmentation.

3D Scene Generation Prior work on 3D object-composed scene generation typically extracts abstract representations such as layouts [55, 56], graphs [57, 58], or segmentations [59, 60, 61, 62, 63, 64] to model object relationships, and then generates [65, 66, 67] or retrieves [68, 68] objects based on these representations and input conditions. The closest work to ours is MIDI [7], which generates compositional 3D scenes by segmenting the input image [69] and prompting object-level 3D diffusion models with region segments. However, MIDI requires all components to be visible in the image, hindering its ability to segment and reconstruct occluded parts, which is a shared limitation across all two-stage segment-and-reconstruct methods. PARTCRAFTER is inspired by MIDI but overcomes this limitation. Without any additional segmentation model, PARTCRAFTER is capable of generating parts even when they are not visible in the conditioning image prompt. PARTCRAFTER demonstrates that explicit segmentation is not a necessary prerequisite for structured 3D scene generation.

3 PARTCRAFTER: A Compositional 3D Diffusion Model

The architecture of PARTCRAFTER is illustrated in Figure 2. It is a compositional generative model that generates structured 3D assets by simultaneously denoising several part-specified latent token sets. Specifically, given a prompt RGB image c and a user-specified number of parts N , PARTCRAFTER generates a structured 3D asset O := { p i } N i =1 , e.g., a part-decomposable object or a 3D scene composed of object parts, where each part p i is a separate, semantically meaningful component of the asset. 3D meshes are utilized as the output representation. At inference time, all part meshes p i := {V i , F i } are generated and decoded simultaneously, where V i ∈ R V i × 3 is the vertex set and F i ∈ N F i × 3 is the face set of the i -th part. Notably, coordinates of each part's vertices are in the same global canonical space [ -1 , 1] 3 , allowing for easy assembly of the parts into a complete object or scene, without any need for additional transformations.

PARTCRAFTER builds upon a pretrained 3D object mesh generation model [1] by utilizing and reassembling its encoders, decoders, and DiT blocks into a compositional multi-entity generation architecture. We provide a description of object-level 3D generation models in Section 3.1, followed by a detailed presentation of our proposed structured multi-entity model in Section 3.2.

Cross Attention

3D Native DiT

Cross Attention

...

Local-Global Attention

Cross Attention

Cross Attention

...

Image

Encoder

3D VAE

Decoder

Noise Latents

(3, 𝐻, 𝑊)

...

Part Mesh

...

...

𝑘/𝑣

+ Part ID.

+ Part ID.

...

...

Global

Attention

+Pos Emb.

Local

Attention

𝑞

Part-decomposable object

3D VAE

Decoder

Patchify

3.1 Preliminary: Diffusion Transformer for 3D Object Mesh Generation

Pretrained 3D object generation models have shown impressive performance in generating highquality 3D objects. Our model specifically builds on top of TripoSG [1], a state-of-the-art 3D image-to-mesh generative model. TripoSG first encodes 3D shapes into a set of latent vectors with a transformer-based Variational Autoencoder (VAE) using 3DShape2VecSet [8]. The decoder of the VAE uses a Signed Distance Function (SDF) representation, which enables sharper geometry and avoids aliasing. A rectified flow model is trained on the V AE latents to generate new 3D shapes from noise. The model is conditioned on a single input image using DINOv2 [70] features, injected via cross-attention in every transformer block. It is trained on 2 million high-quality 3D shapes curated from Objaverse [9, 71] and ShapeNet [10] through a rigorous four-stage pipeline involving quality scoring, filtering out noisy data, mesh repair, and conversion to a watertight SDF-compatible format.

3.2 PARTCRAFTER

The design of PARTCRAFTER is motivated by our observation that foundation 3D latent diffusion models such as TripoSG [1], although trained on object-level training examples, can be applied effectively out-of-the-box to auto-encode 3D parts as well, without normalizing the input 3D mesh to the center of the canonical space (as done for 3D object meshes). We thus adapt the neural components of TripoSG to build a part-decomposable 3D asset reconstruction model.

Compositional Latent Space The key insight of 3D object mesh generative models with a set of latents is that each token in the latent space of the pretrained 3D V AE [1, 2, 8] is implicitly associated with an area of the canonical 3D space. PARTCRAFTER expands the latent space of monolithic object generative models with multiple sets of latents, each taking care of a separate 3D part. Specifically, an object or a scene is represented as a set of N parts O = { p i } N i =1 and each part p i is represented by a set of latent tokens z i = { z ij } K j =1 ∈ R K × C , where K is the number of tokens in the component's set and C is the dimension of each token. To distinguish across parts, we add a learnable part identity embedding e i ∈ R C to the tokens of each part p i . These embeddings are randomly initialized and optimized during training. To support the inherent permutation invariance of the parts, we shuffle the order of the parts in training. Since z i represents the features of the i -th part, we construct the global 3D asset tokens simply by concatenating the tokens of all parts, i.e., Z = { z i } N i =1 ∈ R NK × C .

Local-Global Denoising Transformer We fuse information across sets of latent tokens to enable both local-level and global-level reasoning. We apply local attention independently to the tokens z i of each part. This captures localized features within each part, ensuring that their internal structure remains distinct. After capturing part-level features, we apply global attention over the entire set of

3D Assets with

Part Information

Part Count

3D Assets

Statistics

32

1632

17.4%

10.6%

05'0

14.3%

8~16

26.1%

0.06

15.9%

15.6%

0.04

0.09

tokens Z to model global interactions across parts. The attention operations are defined as:

where A denotes the resulting attention maps. This hierarchical attention structure captures both fine-grained part details and global 3D context, enabling effective information exchange between local and holistic representations. We adopt the DiT architecture with long skip connections [35, 72, 73], as in TripoSG [1], and replace its original attention module with our part-global attention mechanism.

We inject DINOv2 [70] features of the condition image c into both levels of attention. Specifically, we use cross-attention within both the local and global attention. This dual-conditioning design enables the model to align the overall part-based composition with the input image while ensuring that each part remains semantically meaningful. Our design choices are validated in Section 4.3.

Training Objective We train PARTCRAFTER by rectified flow matching [74, 75, 76, 77], which maps the noisy Gaussian distribution to the data distribution in a linear trajectory. Specifically, given an object latent Z 0 = { z i } N i =1 and a condition image c , we perturb the latents by adding Gaussian noise ϵ ∼ N (0 , I ) at a noise level t , yielding a noisy latent representation Z t = t Z 0 +(1 -t ) ϵ . The model is then trained to predict the velocity term ϵ -Z 0 given the noisy latent Z t at the noise level t , conditioned on the condition image c , by minimizing the following objective:

where v θ is the velocity prediction. Importantly, the noise level t is shared across all parts of the 3D scene or object to ensure consistent trajectory sampling.

3.3 Dataset Curation

3D object datasets such as Objaverse [9] and Objaverse-XL [71] have made millions of 3D assets available to the research community. While previous works [1, 3, 38] have primarily focused on directly utilizing these datasets [9, 10, 11, 52, 71, 78] for 3D whole-object generation, we observe that the rich part-level metadata included in many of these 3D models offers an opportunity to enable more structured 3D generation. As shown in Figure 3, over half of the objects in a subset [79] of Objaverse [9] contain explicit part annotations. These part labels often originate from artists' workflows, where objects are intentionally decomposed into semantically meaningful components to facilitate modular design, such as modeling a pair of scissors using two distinct blades. To train our model, we curate a dataset by combining multiple sources [9, 10, 11, 79], yielding 130,000 3D objects, of which 100,000 contain multiple parts. We further refine this dataset by filtering based on texture quality, part count, and average part-level Intersection over Union (IoU) to ensure high-quality supervision. The resulting dataset comprises approximately 50,000 part-labeled objects and 300,000 individual parts. For 3D scenes, we utilize the existing 3D object-composed scene dataset 3D-Front [12]. Additional dataset statistics and filtering criteria are detailed in Appendix A.

3.4 Implementation Details

We modify the 21 DiT blocks in TripoSG [1] by alternating their original attention processors with our proposed local-global attention mechanism. Specifically, global-level attention is applied to DiT blocks with even indices, while local-level attention is used in the odd-indexed blocks, following the long-cut strategy. We validate this architectural design in Section 4.3. PARTCRAFTER is trained on 8 H20 GPUs with a batch size of 256 by fully finetuning the pretrained TripoSG [1]. We first train a base model for up to 8 parts on our curated part-level object dataset at a learning rate of 1e-4 for 5K iterations. For part-decomposable objects, we then finetune the base model to support up to 16 parts. For object-composed scenes, we further adapt the base model to the 3D-Front [12] dataset for up to 8 objects. Both finetuning processes last for 5K iterations at a reduced learning rate of 5e-5. We include 30% monolithic objects in training for regularization. This curriculum training strategy avoids loss spikes and catastrophic forgetting during the training process. The whole training process takes about 2 days. We use 512 tokens for each part, which we find is sufficient to represent part geometry and semantics. We evaluate PARTCRAFTER on a test set of about 2K data samples.

4 Experiments

Our experiments aim to answer the following questions: (1) How does PARTCRAFTER perform in part-level reconstruction of objects and scenes compared to existing state-of-the-art models that first segment and then reconstruct parts at the object and scene level? (2) Can PARTCRAFTER reconstruct parts that are not visible in the image prompt? (3) How do results vary with different numbers of parts? (4) What are the contributions of design choices in our local-global denoising transformer?

Baselines To the best of our knowledge, PARTCRAFTER is the first work to generate 3D part-level object meshes from a single image. Recent works Part123 [4] and PartGen [5] reconstruct 3D neural fields [21, 54] from images, which are not directly comparable to our work that focuses on meshes. We consider the following baselines: (1) HoloPart [6] on object level, which is a concurrent work that first segments a given 3D object mesh and then completes the coarse-segmented parts into fine-grained meshes. We adapt HoloPart to our setting by utilizing TripoSG [1] to generate a mesh from an image, and apply HoloPart to get the part meshes. For fair comparison, we align the number of tokens in TripoSG with our method, that is, N × 512 tokens for N parts. (2) MIDI [7] on scene level, which reconstructs multi-instance 3D scenes using object segmentation prompts. We provide ground truth segmentation masks for MIDI, while PARTCRAFTER does not need any segmentation.

Evaluation Metrics We measure the generation result of structured 3D assets in both the global (object or scene) and part level. (1) Fidelity of the generated mesh. Since we do not have the correspondence between the generated and ground truth parts, we simply concatenate the parts to form a single mesh. We evaluate the fidelity of generated 3D meshes by L2 Chamfer Distance (CD) and F-Score with a threshold of 0.1. Lower Chamfer Distance and higher F-Score indicate higher similarity between the generated and ground truth meshes. (2) Geometry Independence of Generated Part Meshes. We use the Average Intersection over Union (IoU) to evaluate the geometry independence of generated part meshes. We compute the average IoU between each generated part by voxelizing the canonical space into 64 × 64 × 64 grids. Lower IoU indicates less overlap between generated parts, thus demonstrating better part independence. The optimal metrics are reached when generated parts are non-intersecting and can be composed into an object similar to the ground truth. We report the average generation time of objects or scenes with 4 parts in the test set on an H20 GPU.

4.1 3D Part-level Object Generation

We evaluate the performance of PARTCRAFTER on the 3D part-level object generation task. As shown in Table 1, PARTCRAFTER outperforms HoloPart by a large margin in both object-level and part-level metrics. Given an image, PARTCRAFTER is able to generate a 3D part-decomposable mesh with high fidelity and geometry independence in seconds. HoloPart requires substantially more time to segment the object mesh, and its segmentation process suffers due to the lower geometry quality of the generated mesh compared to real artistic meshes, which hinders its performance. Notably, PARTCRAFTER surpasses our backbone model TripoSG [1] in object-level metrics, even when we align the number of tokens in TripoSG with our method. This suggests that our localglobal attention mechanism enables better object-level representation learning, thereby enhancing

3D Part-level

Generation

Objaverse [9]

ShapeNet [10]

ABO [11]

↓

Time

↓

CD

↑

F-Score

↓

IoU

↓

CD

↑

F-Score

↓

IoU

↓

CD

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F-Score

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IoU

Dataset

/

/

0.0796

/

/

0.1827

/

/

0.0137

/

TripoSG [1]

0.3104

0.5940

/

0.3751

0.5050

/

0.2017

0.7096

/

/

TripoSG* [1]

0.1821

0.7115

/

0.3301

0.5589

/

0.1503

0.7723

/

HoloPart [6]

0.1916

0.6916

0.0443

0.3511

0.5498

0.1107

0.1338

0.8093

0.0449

18min

Ours

0.1726

0.7472

0.0359

0.3205

0.5668

0.0293

0.1047

0.8617

0.0243

34s

3D Scene Generation

3D-Front [12]

3D-Front (Occluded) [12]

↓

Run Time

↓

CD

↑

F-Score

↓

IoU

↓

CD

↑

F-Score

↓

IoU

MIDI [7]

0.1602

0.7931

0.0013

0.2591

0.6618

0.0020

80s

Ours

0.1491

0.8148

0.0034

0.1508

0.7800

0.0035

34s

the generation process through improved structural understanding . We also observe that PARTCRAFTER performs worse on ShapeNet [10] than on Objaverse [9] and ABO [11], primarily due to the backbone's degraded performance on ShapeNet. We present qualitative results in Figure 4 to show that PARTCRAFTER can infer parts invisible in the conditioning image and generate 3D part-level objects. We further provide 3D object texture generation results in Appendix D.

4.2 3D Object-Composed Scene Generation

Weconduct experiments on 3D scene generation using the 3D-Front [12] dataset. As shown in Table 2, PARTCRAFTER outperforms MIDI [7] in reconstruction fidelity metrics. While MIDI [7] uses ground truth segmentation masks for evaluation, PARTCRAFTER does not require any segmentation. To further validate our method, we select a subset of 3D scenes in 3D-Front [12] with severe occlusion, where the ground truth segmentation masks cannot segment all objects. We observe that the performance of MIDI [7] degrades significantly in these cases, while PARTCRAFTER still maintains a high level of generation quality. MIDI [7] slightly surpasses PARTCRAFTER in IoU metrics, primarily due to the fact that ground truth 2D segmentation masks are used in their pipeline. Qualitative results are provided in Figure 5 to demonstrate that PARTCRAFTER can recover complex 3D structures and generate high-quality meshes for 3D scenes, all from a single image.

For additional qualitative results of our method, please check Appendix E and the supplementary file.

4.3 Ablations

We present quantitative results in Table 3 to validate the effectiveness of each component in our method. All experimental settings are consistent with our base model, using a maximum of 8 parts and 512 tokens per part, trained for 5K iterations with a learning rate of 1e-4 and a batch size of 256.

Necessity of Local-Global Attention To assess the necessity of local-global attention, we conduct ablation experiments by removing either the local attention or the global attention. We observe that the model completely collapses without local attention and fails to generate any meaningful 3D mesh (Exp. 2). In contrast, removing global attention still allows the model to produce 3D meshes, but the outputs lack part decomposition, exhibit significant geometry overlap, and result in notably higher IoU values (Exp. 3). These findings demonstrate that local-global attention is essential for learning meaningful 3D representations and capturing the 3D structural relationships globally.

Input

HoloPart

Ours

Input

HoloPart

Ours

Input

MIDI

Ours

Input

MIDI

Ours

Necessity of Part Identity Control We remove part identity embeddings from attentions, and the model collapses due to ambiguity in distinguishing between different parts (Exp. 1). We further explore how to incorporate control signals into the local-global attention (Exp. 4). Enabling crossattention in the local attention improves mesh fidelity but reduces geometry independence, while enabling it in the global attention enhances geometry independence at the cost of fidelity. Based on these observations, we adopt cross-attention in both local and global attention modules to strike a balance between fidelity and independence, achieving the best overall performance.

Order of Local-Global Attention Since our DiT follows a U-Net-like architecture with long skip connections, we investigate how the ordering of local-global attention affects performance (Exp. 5). Across the 21 DiT blocks, we approximately balance the number of local and global attention modules. We explore three different configurations for placing global-level attention: (1) in the middle, (2) at the beginning and end, and (3) in an alternating pattern. Among these, the alternating configuration

Input

1 Part

2 Parts

3 Parts

4 Parts

5 Parts

6 Parts

7 Parts

8 Parts

yields the best trade-off between local-level and global-level metrics, suggesting that this arrangement enables more effective information exchange and integration across both levels.

Specified Number of Parts We present qualitative results in Figure 6 to show that PARTCRAFTER can generate reasonable results with a varied number of parts given the same image. It validates that PARTCRAFTER captures 3D features hierarchically and handles different granularities of part decomposition, which can be a useful feature for downstream applications and commercial use.

5 Conclusion

In this work, we propose PARTCRAFTER, a novel 3D native structured generative model. Trained on our curated part-level 3D mesh dataset, PARTCRAFTER reconstructs part-level objects and scenes without relying on any 2D or 3D segmentation information that existing models need. PARTCRAFTER validates the feasibility of integrating 3D structural understanding into the generative process.

Limitations and Future Works PARTCRAFTER is trained on 50K part-level data, which is relatively small compared to that used to train 3D object generation models (typically millions). Future works can consider scaling up DiT training with more data of higher quality.

Broader Impact We conducted a foundational study in 3D computer vision. While our large-scale training may have environmental implications, we believe the benefits of advancing 3D vision justify further exploration. The importance of the problem studied is discussed in the Introduction.

Acknowledgment

This research is supported by a grant from Bytedance PICO (No. CT20240607105793).

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A Dataset Details

We collect our part-level dataset from Objaverse [9] (ODC-By v1.0 License), ShapeNet-Core[10] (Custom License), and Amazon Berkeley Objects [11] (CC-BY 4.0 License). We use a high-quality subset of Objaverse provided by LGM [79]. We filtered out objects without textures and selected those with fewer than 16 parts and a maximum IoU of less than 0.1. The resulting dataset comprises approximately 50,000 part-labeled objects and 300,000 individual parts. We adopt an additional 30,000 monolithic objects as regularization. We use the 3D-Front (CC-BY-NC 4.0 License) dataset processed by MIDI [7]. We will release our dataset under a CC-BY 4.0 license.

B Implementation Details

Our model builds on TripoSG [1] (MIT License). As for part-level object generation, we adapt HoloPart [6] (MIT License) into a generative pipeline. Specifically, we first generate a 3D mesh from the input image using TripoSG and then use HoloPart to generate a part-level object. We use the same 3D segmentation model, SAMPart3D [80] (MIT License), as Holopart. As for 3D scene generation, we adopt MIDI [7] (MIT License) as a baseline. We will release our code under an MIT license.

C Real-world Results

We train PARTCRAFTER on synthetic rendered images, which allows us to have a large and diverse dataset. To test the boundary of our model's generalization ability, we test PARTCRAFTER on realworld images from the CO3D [81] dataset. As expected, the performance was lower compared to synthetic images due to the domain gap. To address this, we explored transferring the style of realworld images to make them look more like images rendered from a graphics engine using recent image editing models, such as GPT-4o. Specifically, we use the prompt: 'Preserve all details and perform image-to-image style transfer to convert the image into the style of a 3D rendering (Objaverse-style rendering). ' We find that our model performs significantly better in the style-transferred images. Therefore, we propose a two-stage pipeline for real-world image inference: first, use an image editing model to transfer the style of the input image, and then use PARTCRAFTER to generate the 3D part-level object. As shown in Figure 7, this simple pipeline works surprisingly well.

D Texture Generation

As shown in Figure 8, we further generate textures for the generated 3D part-level objects using an off-the-shelf texture generation model Hunyuan3D-2 [38] (Hunyuan3D-2 License). Although Hunyuan3D-2 is trained on monolithic objects, it performs well on part-level objects. Since we know which part each vertex belongs to, we can manipulate the generated UV map accordingly, assigning textures to their corresponding parts. Therefore, our method is capable of generating 3D objects composed of multiple distinct parts with respective textures, all from a single image . Compared to previous methods that generate shape and texture for monolithic objects, our method produces part-aware 3D objects with distinct textures for each component. Because prior approaches treat the object as a single whole, textures often suffer from artifacts such as color bleeding between parts, and the resulting shapes may lack physically plausible part-level structure. In contrast, our method ensures more accurate texture assignment and generates 3D structures that better reflect the compositional nature of real-world objects. Thanks to these features, we believe PARTCRAFTER holds great potential for downstream applications such as real-to-simulation transfer and robotics training, where accurate and part-aware 3D representations with textures are crucial.

E More Results

We provide more generation results on 3D part-level object in Figure 9, 10, 11, and 12 and 3D object-composed scene in Figure 13. For better visualization results, please see the supplementary files and our project website: https://wgsxm.github.io/projects/partcrafter/ .

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces PartCrafter, a novel structured 3D generative model that creates semantically meaningful and geometrically distinct 3D parts from a single RGB image—without requiring pre-segmented inputs. This paper proposes a unified, end-to-end architecture based on a compositional latent space and hierarchical attention. It builds on a pretrained 3D diffusion transformer, enabling generation of coherent multi-part objects and scenes. A new dataset with part-level annotations is also curated to support training. Experiments show PartCrafter outperforms previous approaches in both single object and multi-object scenarios.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n- The paper demonstrates that foundation 3D latent diffusion models (e.g., TripoSG) can effectively auto-encode 3D parts without requiring centered input meshes. This insight enables the introduction of a compositional latent space with multiple 3D part latents, allowing end-to-end generation of multi-part objects without pre-segmented inputs.\n- The authors contribute a valuable dataset with part-level annotations that will benefit future research in this domain.\n- The proposed method achieves superior fidelity in generated 3D parts compared to existing state-of-the-art approaches.\n\n**Weaknesses:**\n- While the end-to-end approach is innovative, the controllability of the model remains a concern. Although Figure 6 demonstrates the model's ability to vary the number of generated parts, it is unclear whether users must experiment with different part counts during inference to achieve desired results.\n- The semantic consistency of generated parts appears inconsistent across different objects. For instance, Figure 10 shows that ears are sometimes connected to the head and sometimes disconnected, indicating potential issues with part boundary consistency.\n- The paper lacks evaluation on in-the-wild images, limiting assessment of real-world applicability.\n- The paper does not address part editing capabilities, particularly for scenarios where users wish to modify non-isolated parts (e.g., editing a foot that is connected to a leg). This represents a significant practical limitation.\n\nQUESTIONS:\nI would recommend the authors to consider the following questions:\n\n1. During evaluation, how is the number of generated parts determined? Is this parameter fixed across all experiments when comparing with baseline methods, or does it vary depending on each individual object?\n\n2. The paper currently uses IoU metrics to measure the geometric independence of generated part meshes. However, HoloPart employs IoU to evaluate the semantic accuracy of generated parts by comparing them against ground truth parts. Could the authors consider incorporating similar semantic accuracy evaluation metrics to provide a more comprehensive assessment of part generation quality?\n\n3. Please provide additional discussion regarding the controllability of the model, particularly addressing how users can effectively control the generation process during inference.\n\n4. It would be valuable to include more extensive results demonstrating in-the-wild generation capabilities and part editing functionality to better assess the method's practical applicability.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposed a method to generate 3D objects from single view image with part-level decomposition. There are two key innovations: (1) A compositional latent space where each 3D part is represented by a set of disentangled latent tokens; (2) A hierarchical attention mechanism that enables structured information flow both within individual parts and across all parts. The experiment shows the good performance of the proposed method.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The method show promising results\n2. The ablation is comprehensive. \n\nWeakness:\n1. concerns about data leak: \n1.1 the method is initialized with TripoSG, which utilized many data sources for training their model including objaverse and objaverse xl, so that even the author made a test split from their filtered data, the objects are highly likely have been part of the training set of its pretrained backbone model. \n1.2 Weapon, axe, chairs, sword are really very abundant in the objaverse dataset, and many of such objects are just the same whitebox mesh with different textures, so that it is not safe and convincing to demonstrate the effectiveness with these objects.\n\n2. Fairness of evaluation: The paper reported 3D Part-level Object Generation result on Objaverse (Table 1). It includes TripoSG while TripoSG was trained on Objaverse. Therefore, it is weird if the method can perform better than TripoSG*. However, the author explains in the paper that the evaluation was performed on part-level objects, which implies that the GT objects all have inner (occluded) structures. However, TripoSG was trained on watertight objects with no inner structure. This may present a significant unfairness in evaluation.\n\n3. It would be good if a pipeline figure can be self-contained. However, it is not clean what does Part-decomposable object and Monolithic object means and what is their purpose if we only read the figure and its caption.\n\n4. insufficient evaluation: following weakness 1, the presented visual results are mainly cartoon-style, game-style, or simple, static, convex and common real-world objects. This may be insufficient to demonstrate that the proposed method is truly effective on real use cases. Usually, many method claimed good performance fails if being tested out of the above scoop. To demonstrate that the proposed method is really effectiveness, the author should present more examples, including 1) images captured in real-world, 2) objects with some dynamics (e.g. a realistic-style image about a dog running to the camera); 3) objects with complex spatial inter-part relationship.\n\nQUESTIONS:\n1. The paper only presented good cases. What would be the border of the model capability?\n2. Please explain in more details the evaluation process for Table 1. Specifically, are there inner structures in the GT mesh, and does this incurs unfairness that make TripoSG have disadvantage in metrics.\n\nLIMITATIONS:\nNo obvious limitation.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes the first part-aware native 3D generator. The model builds upon a pre-trained whole-object generator and introduces two key innovations: a compositional latent space and a local-global attention mechanism. In addition, the authors curate a 3D dataset with part annotations to facilitate training. Experimental results demonstrate the model's superiority over traditional two-stage pipelines in terms of both efficiency and part segmentation quality.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n\n- The paper presents a novel approach to part-aware 3D generation. Unlike conventional two-stage pipelines, the proposed method generates part-decomposable objects in an end-to-end fashion. This design enhances both efficiency and segmentation quality.\n\n- The experimental evaluation is comprehensive, covering both object- and scene-level domains. The results show significant improvements in part segmentation quality compared to prior methods.\n\n- The paper is well-organized and clearly written, making it easy to follow.\n\n## Weaknesses\n\n- The paper lacks a detailed analysis of how the number of parts affects both time cost and generation quality. For example, in Figure 6 (Lego figurine), the hand shapes deteriorate in the 2-part and 4-part settings, indicating instability.\n\n- The model’s ability to handle occluded or invisible parts at the object level remains uncertain. For instance, in Figure 2, the left arm in the conditioning image is partially occluded by fire, and the generated 3D asset fails to reconstruct a complete left arm.\n\n- The paper lacks failure cases.\n\n- The choice of evaluation metric could be improved. While IoU is used to evaluate part-level quality, it may not capture semantic correctness. For example, parts with low IoU might be semantically meaningless.\n\nQUESTIONS:\n- How does the number of parts affect the model’s performance in terms of both generation quality and computational cost? A more thorough analysis would be helpful.\n\n- Could you provide more details on how part annotations were mined from Objaverse? Given that its file structure is artist-defined and hierarchical, how was this handled to ensure consistency?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a method to unify the architecture for generating decomposable meshes conditioned on an image. Concretely, the authors propose a one-stage pipeline to generate part mesh instead of following a first segment, then repair the parts paradigm. The method is based on the diffusion network, and the proposed compositional latent space to enable global and local attention for information within and across parts. The author also curated a part-based dataset from open-source datasets, as claimed to be part of the contribution. The authors also compare both single mesh and scene-based part generation with SoTA methods like HoloPart.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n--The work adopts TripoSG’s 3D VAE representation, which associates input point cloud locations to a set of latents; thus, the parts can use a shared canonical space instead of renormalizing into a canonical space separately. This representation allows the model to generate parts with transformation baked in and does not need to predict additional transformation information for each part.\n\n--The adaptation of global and local attention to model global part interaction and per-part details is novel. The author also provides a detailed ablation study on the importance of using dual attention.\n\nWeakness:\n--The author adapts the TripoSG VAE representation and found that even when the VAE is trained on a single mesh, it can still reconstruct part-based meshes. This is a strong assumption, but there is no validation in the experiment to prove it. It would be great to show the reconstruction accuracy for parts with TripoSG VAE to validate the assumption.\n\n--The current pipeline cannot control the number of parts and the semantic labels, which are important in practice.\n\nQUESTIONS:\n--The part dataset is constructed by parsing the node, how to make sure the node grouping is meaningful?\n\n--In Figure 6, it shows the model can generate variable length of parts, is the training also randomly select the number of parts, or does it learn to adapt to the number of parts?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

comments: SUMMARY:
This paper proposes a framework to generate 3D meshes with parts using diffusion models directly. The model encodes different parts of the objects into different sets of latent vectors, learning an image-conditional diffusion model in the latent space. The diffusion model utilizes local attention within parts and global attention across parts. The paper shows applications in generating part-level meshes with image condition and 3D object-level scene generation.

STRENGTHS AND WEAKNESSES:
Pros:

1)The experiments ablate different components of the framework. 

2)The generated shapes have high quality, and the details of the shapes are well preserved. 

3)Inference speed is fast. 

Cons:

1)There is not enough description about the details of the VAE. What do the encoder and decoder look like? 

2)Missing details of the model design, for example, 
- what’s the value of C (dimensions of the tokens)? 
- How many vertices and faces are generated? 
- Are you using a different number of vertices/faces per shape? How is that controlled in inference?

QUESTIONS:
1)For training samples that have fewer or more parts than the hyper-parameter N, how do you deal with it?

2)How do you control the number of parts in inference? For example, for a model trained with Z = {z_i} ∈ ℝ^{NK×C}, how do you generate fewer than N parts in inference? What does the shape of Z look like in that case? 

3)Is the model able to generate parts inside the objects?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 1
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-07-03