Efficient Part-level 3D Object Generation via Dual Volume Packing
Authors: Jiaxiang Tang
Ruijie Lu
Max Li
Zekun Hao
Xuan Li
Fangyin Wei
Shuran Song
Gang Zeng
Ming-Yu Liu
Tsung-Yi Lin
Venue: NeurIPS 2025 poster ()
Keywords: 3D Generation
Part Generation
Image-to-3D
Primary area: applications
TL;DR: We present a method to generate high-quality 3D shape composed of individual and complete parts from a single-view image.

Abstract: Recent progress in 3D object generation has greatly improved both the quality and efficiency.
However, most existing methods generate a single mesh with all parts fused together, which limits the ability to edit or manipulate individual parts.
A key challenge is that different objects may have a varying number of parts.
To address this, we propose a new end-to-end framework for part-level 3D object generation.
Given a single input image, our method generates high-quality 3D objects with an arbitrary number of complete and semantically meaningful parts.
We introduce a dual volume packing strategy that organizes all parts into two complementary volumes, allowing for the creation of complete and interleaved parts that assemble into the final object.
Experiments show that our model achieves better quality, diversity, and generalization than previous image-based part-level generation methods.
Our project page is at \url{https://research.nvidia.com/labs/dir/partpacker/}.

Efficient Part-level 3D Object Generation via Dual Volume Packing
Jiaxiang Tang 1.2+
Ruijie Lu
Zhaoshuo Li²
Zekun Hao?
Xuan Li² Tsung- Yi Lin"
Fangyin Wei
Shuran Song 2.3
Gang Zeng'
Ming- Yu Liu
State Key Laboratory of General AI, Peking University
NVIDIA Research, SStanford University
Abstract
Recent progress in 3D object generation has greatly improved both the quality and efficiency. However, most existing methods generate a single mesh with all parts fused together, which limits the ability to edit or manipulate individual parts. A key challenge is that different objects may have varying number of parts. To address this, we propose new end-to-end framework for part-level 3D object generation. Given a single input image, our method generates high-quality 3D objects with an arbitrary number of complete and semantically meaningful parts. We introduce dual volume packing strategy that organizes all parts into two complementary volumes, allowing for the creation of complete and interleaved parts that assemble into the final object. Experiments show that our model achieves better quality, diversity, and generalization than previous image- based level generation methods. Our project page is at https: : //res/eeerr//e/eee ch, .vidia. com/ labs/ babs/dir/pen htt///ir//rrrr/// htt.r///apttaee////////
Introduction
Part-level 3D generation focuses on creating objects composed of multiple distinct and semantically meaningful parts, which crucial for downstream editing and manipulation in applications such as game development and robotics. Although recent methods have greatly improved the quality of 3D object generation, they typically produce fused shapes without explicit part structures [55, 56, 59, 5, 22, 54,57. 57]. Generating complete and meaningful 3D parts remains major challenge. It requires deeper understanding of both the global layout and local part interactions within the object, which current methods still struggle to model effectively.
Several recent methods [4, 52, 51, 28] have explored part-level 3D generation by first segmenting the fused mesh into incomplete parts (usually surface patches), and then applying reconstruction or completion models to each part individually. While these pipelines have achieved promising results, they suffer from two fundamental limitations. First, they rely heavily on external segmentation priors, often derived from 2D models or pretrained networks. This introduces additional preprocessing steps and poses risk of error propagation any mistake in the segmentation stage can negatively impact the final generation quality. Second, these methods process each part sequentially, resulting in inefficiencies during inference. As the number of parts increases, inference time scales linearly, regardless of the individual complexity each part. These limitations underscore two core challenges in part-level 3D generation: handling an unknown and variable number of parts, and mitigating the inefficiency of part-by-pant sequential processing.
In this paper, we present novel approach for directly generating part-level 3D objects without relying on any 2D or 3D segmentation priors. Our framework enables end-to-end generation of
"This work is done while interning with NVIDIA.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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30s
Single-view Image
Part-level Shape
Complete Parts
Generalization Capability
Joint Articulation
Editing
Figure 1: End-to-end Part-level Image-to-3D Generation. We present method generate 3D shape composed of individual and complete parts from a single-view image. Our method is trained only with 3D native information and can generate part-level meshes about 30 seconds without relying on 2D segmentation prior models.
an arbitrary number of parts within fixed time. We identify the key challenge to be the handling of overlapping regions between contacting parts. In contrast, disjoint parts, which are naturally separable as different connected components, This observation motivates our part- sart-packing strategy, which aims to pack as many disjoint parts as possible into shared volume to maximize space utilization, improve generation efficiency, and avoid fusing contacting parts. By analyzing the connectivity patterns commonly found in real woaldwordd objects, observe that many can be effectively partitioned into two groups. We therefore formulate this task as bipartite contraction problem and introduce a dual volume packing strategy, which fixes the output length and is fully compatible with existing 3D latent denoising models [56, 59].
be processed in parallel rather than sequentially.
In summary, our contributions are as follows:
1. We propose novel end-to-end part-level 3D generation framework, which produces high- quality 3D shapes with an arbitrary number of semantically meaningful parts from a single input image, without relying on any segmentation prior.
2. We introduce dual volume packing strategy that converts the part-connectivity graph into bipartite graph through heuristic edge contraction, enabling efficient use of 3D volumes while preserving separation between contacting parts.
3. Experiments show that our method achieves more robust, diverse, and structurally consistent levt-level generation compared existing approaches, while offering a simpler and more efficient generation pipeline.

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2 Related Work
2.1 3D Denoising Generative Models
3D-native denoising models for conditional 3D generation have seen substantial progress in recent years. Early research efforts focused on uncompressed 3D representations, such as point clouds [32], Neural Radiance Fields (NeRFs) [30, 17, 41], volumetric representations [13, 10, 33, 60, 31, 2, 26, 50], and other formats [27, 3, 53, 47J. Despite their potential, these methods face limitations when applied to small or sparse datasets, often resulting in poor generalization and suboptimal quality.
More recent work has shifted towards latent denoising models for 3D generation [56, 57, 45, 21, 20, 16, 38, 9, 46, 22, 59]. These approaches typically employ VAE to compress 3D data into a compact latent space, facilitating more efficient training of the denoising generative models. 3eshape2Vecset [55] first introduced vector-set-baa latent representation for effective 3D com- pression and generation. CLAY [56] demonstrates that combining VAE and 3D latent diffusion transformers yields strong performance on large-scale datasets and supports diverse conditioning modalities. TripoSG [22] pioneers the use of latent rectified flow [25, 24] and a mixture-of offrre-ff---ttt----ee architecture for 3D generation. Trellis [46] designs sparse, structured latent representation with multi-format VAE decoders to support efficient rectified flow modeling. Hi3DGen [54] proposes normal-bridgs mechanism enhance surface detail reconstruction. Dora [5] introduces salient edge sampling to improve the VAE's reconstruction fidelity, while Hunyuan3D-22 [59, 19] explores efficient network architectures to improve generation quality and accelerate VAE decoding. this work, we extend such 3D latent denoising models to support part-level generation.
2.2 Part-level 3D Generation
While existing 3D latent denoising models can generate detailed geometry, their outputs are typically fused meshes extracted from occupancy or SDF fields, where all parts are fused together. However, many practical applications require part-level 3D meshes to support editing and manipulation. A core challenge in part-level 3D generation is the varying number of parts per object, whereas latent denoising models usually rely on a fixed-length latent code, making such variability difficult to handle.
One promising direction is employ auto-regressive models, where next-token prediction naturally accommodates variable-length outputs. For instance, MeshGPT [37] introduces this concept by tok- enizing mesh using face sorting and VQ-VAE- compression, followed by an auto-regressive transformer to predict the token sequence. Subsequent works [6, 43, 7, 8, 14, 40, 23, 58, 42, 44] expand on this idea by designing various mesh tokenization strategies and conditioning mechanisms, including point clouds and single-view images. However, these methods often face a major limi- tation: complex meshes typically contain large number of faces, making the generation process computationally expensive and tmee-conummng.
frameworks that leverage 2D seg- mentation prior models [18, 35J. PartGen [4] adopts multi-view diffusion model combined with 2D segmentation to generate multi-view part segmentation maps, which are subsequently used for multi-view completion and per-part 3D reconstruction. SAMPart3D [51] proposes a zero-shot 3D part segmentation approach that transfers part-level knowledge from 2D priors the 3D domain, enabling multi cullti-rranularity segmentation. PartField [28] learns feed-forward 3D feature field that encodes part semanties and hierarchical structure, allowing for clustering-bart based segmentation and shape-level correspondence. HoloPart [52] introduces a specialized 3D latent denoising model that completes each segmented part by attending to both local geometry and global context, thereby preserving structural consistency during reconstruction. Despite their effectiveness, these methods often rely on 2D segmentation priors, which may introduce propagation errors when the input segmentations are inaccurate inconsistent. Moreover, their pipelines tend to be complex and inefficient, typically requiring iterative processing over individual parts. To overcome these limitations, we propose an end-to-end image-to-3D generation framework that directly produces levt- 3D meshes.
Another line of work focuses on segmentationn-then-commm
3

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Bipartite Graph Construction
Dual Volume Packing
Figure 2: Dual Volume Packing. Given 3D mesh with part-level annotations, we propose convert the part-connectivity graph into a bipartite graph, such that all parts can be packed into two volumes. Within each volume, parts do not contact each other, thus can be separated during mesh extraction.
3 Methodology
Similar to previous 3D latent denoising models [56, 22, 59, 54, 46), our model takes a single-view image imput and generates the corresponding 3D shape. In contrast, our goal is to predict separable and complete 3D parts that can be assembled into the final shape, rather than producing a single fused mesh. We begin by introducing our dual volume packing strategy (Section 3.1) and describing the process for curating a part-level dataset (Section 3.2). We then detail the architecture of our part-level 3D generation model and the associated training procedures (Section 3.3).
3.1 Dual Volume Packing
Most current 3D generation methods rely on volumetric representations, such SDF grids, to represent shapes. Given an input image, these methods generate single 3D volume, from which meshes can be extracted using iso-surfacing algorithms [29]. We observe that the key difficulty in part-level 3D generation lies the handling contacts between parts. If two parts are not in contact within the 3D space, they naturally form separate connected components in the extracted mesh and can be easily isolated. However, when parts are in contact, they become fused in the SDF grid, causing the loss of individual part information and making separation infeasible.
This observation motivates our concept of volume packing. Instead of assigning each part separate volume, we propose packing as many disjoint parts as possible into the same volume to maximize spatial efficiency and minimize computational cost. This problem can be formulated as standard graph vertex coloring task. We construct part corresponds to a vertex V, and each pair of contacting parts forms an edge in E. The goal is to assign colors to vertices such that no two adjacent vertices share the same color, while minimizing the total number colors. Parts with the same color can then be safely packed into a shared volume.
conteeeonettiitty graph g = V,E, where each 3D
However, this strategy alone does not fully address the issue of varying output lengths. The upper bound of the chromatic number (G) depends on factors such as the graph 's maximum degree and the presence of odd cycles. As illustrated in Figure 2, we further observe that many 3D objects have simple cart-connectivity graphs with bipartite structure, where GG)
This insight leads us to adopt dual volume packing scheme, which is especially suitable for efficient pert-level 3D latent denoising models. The output length becomes fixed, as we only need to generate two volumes, and there are only two valid colorings for each connected bipartite graph. Although it is possible to use more than two volumes, doing so introduces practical challenges: (1) When xG) 2 3, the graph often has multiple valid colorings, and the permutation invariance of color assignments complicates the learning process. (2) Using additional volumes results in lower space utilization per volume, which reduces overall efficiency. For these reasons, we adopt the dual volume packing strategy when curating our dataset.
Graph Extraction. For meshes whose part-connectivity graphs are not bipartite, we apply a sequence of edge contraction operations (i.e., merging specific pairs of parts) to transform the graph into a bipartite one. However, identifying the optimal set of edges to contract is known to be NP-hard [15]. We propose to use a heuristic algorithm specifically designed for the part-connectivity structures observed in meshes. Since bipartite graphs must not contain odd-length cycles, our method performs contractions to eliminate such cycles. To build the input graph, we conduct collision detection and connect edge between each pair of collided parts. We slightly dilate each part
Bipartite

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according to the resolution of the SDF grid to ensure that adjacent parts with tangential contact also form edges. The penetration depth between parts used as the edge weight w(e), with the intuition that parts with more collision should he fused. We then apply depth-first search (DFS) algorithm to identify all cycles the graph. To eliminate odd-length eycles, we greedily iterate over each odd cycle and contract the edge with the greatest penetration depth, effectively merging two vertices and converting the cycle into an even-length one. However, since some edges may be shared across multiple cycles, contracting single edge can affect other cycles, potentially introducing new odd-length cycles. Therefore, we repeat this process over all cycles multiple times until no odd-length eycles remain. The full algorithm is provided in the supplementary materials.
3.2 Part-level Data Curation
Our packing algorithm requires 3D meshes with part-level annotations. To prepare the dataset for training. we perform part extraction, repair, and filtering to curate part-level data.
Part Extraction. As the 3D meshes are stored in GLB format, we extract their scene graphs and use them the primary source of part annotations. In particular, for animated meshes, the geometry nodes typically correspond to animatable parts, serving as meaningful part annotations. However, many meshes contain only single geometry node, with all geometric components fused into one. In such cases, we fall back on using connected components as a proxy for part annotations. Nevertheless, semantically meaningful part may consist of multiple connected components. This issue is especially prevalent when the mesh has undergone UV unwrapping, which introduces seams in the otherwise watertight surface to flatten the 3D geometry into 2D space, resulting in multiple connected components and boundary loops. To address this, we apply a series of empirical post- processing rules: (1) We identify boundary loop pairs that share identical vertices, suggesting they originate from the same watertight surface, and merge the corresponding connected components into single part. (2) Very small connected components are merged into an adjacent contacted part. (3) Pairs of connected components with high intersection-owe ooer--rue-noonn (IoU) score are merged into one part. While these rules do not perfectly recover ground-trui ttuthh part annotations, they are sufficient for our training purposes.
When creating 3I) models, artists may omit faces in occluded regions, resulting in non-watertight meshes. This issue is particularly common when meshes are separated into parts, leading to many won-watett segments. However, VecSet-based VAEs [55] assume watertight meshes in order to learn a balanced signed distance field (SDF). A common workaround is to dilate the surface into thin shell surrounding the geometry [56, 5], creating a watertight shape. While this technique ensures watertightness, it is undesirable because it distorts the original geometry preferred by artists and introduces unbalanced distribution of SDF values. Instead, we propose stitching certain boundary loops exposed during part extraction. This operation reduces the number of non- non-watertight parts and improves the balance of the resulting SDF grid for training.
Part Repair.
Data Filtering. Given the two volumes of parts, ideally the amount of occupied space within each volume are roughly the same for training stability. However, some data samples may still contain extremely unbalanced distribution after the heuristic part extraction and repair processes. To ensure dataset quality, we introduce filtering step remove the unbalanced examples. Let 0₁ and 02 denote the occupancy ratios of the two packed volumes. We discard samples that satisfy the following condition:
((0 < 0.001) A (o2 < 0.001)) v (min(()
min((1, 02) < 0.1
(I)
max 02)
This criterion effectively filters out data with highly unbalanced SDF grids, which are difficult to leam and less useful for model training.
3.3 Dual latent Generation
Our model builds upon several designs from previous VecSet-based latent denoising models [55], notably incorporating the salient edge sampling strategy from Dora [5] and the rectified flow model from Trellis [46]. As illustrated in Figure 3, the model is composed of three main modules. A VAE encodes the packed volumes into a compact latent code, which can later decoded into separable mesh parts. DINOv2 [34] is used as the image encoder to extract conditional features for
5

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Condition
DINOV2 Encoder
Flow
Model
Decader
Input Image
Noises
Dual Latents
Dual Volumes
Part-level Mesh
Figure 3: Network Architecture. Our model takes single-view image the input condition, and generate the dual latents at the same time with a flow model. The latents are decoded to dual volumes, which can be divided into parts and assembled back the whole mesh.
cross-attention. Finally, rectified flow model is trained to denoise the latent codes conditioned these image features.
VAE Model. The VAE consists of dual cross-attention encoder [5] and a self- decoder. Both uniform point samples and salient edge point samples are fed into the encoder. We primarily stack self-attention layers in the decoder and omit them in the encoder to accelerate the encoding process, which is invoked repeatedly during flow model training. The encoder's intermediate features are compressed into latent code. Following previous works [22, 59, 51, train the VAE with multiple latent code sizes to support progressive training of the flow model and to improve convergence speed.
Flow Model. The flow model consists of stack of attention layers, following similar designs as in [22, 59, 56]. key difference is that we denoise a pair of latent codes simultaneously, rather than single one. The two latent codes are concatenated, allowing information exchange through the attention layers. To differentiate between them, we add learnable part embedding exclusively the second latent code as we find this helps to reduce duplicate parts. During inference, both latent codes are predicted jointly and decoded into two separate volumes, each containing disjoint mesh parts. These parts are then assembled to reconstruct the complete 3D shape.
Experiments
4.1 Implementation Details
Dataset. We use the Trellis500k subset [46] of the Objaverse-XL XL dataset [12, 11] (ODC-BY v1.0 license). After applying our part extraction process (Section 3.2), approximately 386K meshes with more than one part remain. Further filtering results in around 254K meshes with well-balanced SDF grids, which are used for training our model. Specifically. we pack the extracted parts into two separate volumes and follow the data preparation pipeline established in prior work [5]: (1) Each volume converted into a watertight mesh [56], and are then used to extract uniform surface samples, salient edge samples, and point-SDF pairs to train the VAE [5]. All meshes are normalized to the 0-995, 0.95]3 cube, and watertight conversion is performed at resolution of 5123, with the dilation threshold set to the voxel size. (2) Each mesh is rendered from multiple camera viewpoints to serve as the conditional input images. Following [46], we do not align the image poses with the geometry. Instead, the model is trained to learn pose-invariant generation by mapping diverse viewpoints to the canonical space.
Our model is trained multiple stages. We first pretrain the base VAE and flow model using the fused shape dataset without incorporating part-level information. The VAE is trained at multiple latent sizes on 64 A 100 GPUs over the course of approximately one week. Next, the flow model is trained progressively with increasing latent sizes [56, 59, 5J. This progressive training phase spans about two weeks using at most 256 A 110 GPUs. In the part-level stage, we observe that the VAE occasionally produces artifacts when reconstructing small parts. To mitigate this issue, we first finetune the VAE using pert-level shapes. For flow model finetuning, we use the largest latent size, resulting in a total latent dimensionality of 4096 = 8192. All parameters are inherited from the pretrained model except for the newly introduced part embedding layer. The finetuning takes about two weeks using 256 100 GPUs. Please refer to the supplementary materials for additional implementation details.
Training.

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OK
OOK
N
OK
OK
Input Image
TripoSG
Hi3DGen
Ours
Figure 4: Comparison on Image-to-3D Generation. Our method generates part-level meshes with competitive quality from single-view images compared to previous methods.
4.2 Qualitative Comparisons.
Our model a latent denoising framework for image-to-3D generation, following the design principles of previous works [22, 46, 54, 59, 5]. We first evaluate the quality of 3D generation from single- view images. Figure 4 presents a qualitative comparison between our results and those generated by TripoSG [22] and Hi3DGen [54]. Our model achieves comparable superior visual quality, demonstrating stronger alignment with the input image and producing more aesthetically pleasing mesh surfaces.
Unlike these baselines, which generate a fused shape in a single volume, our model can directly generate individual parts from single-view image in an end-to-end manner. For part-level 3D generation, we primarily compare against HoloPart [52]. TripoSG [22] is used to generate the fused 3D mesh. We find that the original segmentation methods [51, 39] suggested by HoloPart and error-prone so we apply PartField [28] to obtain 3D segmentation masks. Following [28], we with different cluster counts (number of parts) for each object and select the most reasonable one manually. As shown in Figure 5, our method produces more reasonable parts.
slow
Furthermore, since our model is based on the rectified flow framework [25, 24], by varying the initial noise, we can generate diverse outputs from the same input. Figure showcases examples of diverse generation results under different random seeds.
4.3 Quantitative Comparisons.
We also evaluate the generation quality of image-to-3D methods from single-vier view images. Following Hunyuan3D-2.0 [59], we adopt ULIP [48, 49] and Uni3D [61] as evaluation metrics. These models learn unified representations across text, image, and point cloud modalities, enabling cosine similarity- based comparisons between generated 3D shapes and reference images. To perform the evaluation, we curate test set of 40 images sourced from diverse domains, and use each method to generate corresponding 3D meshes. To ensure fair comparison, we the number of denoising steps to 50, extract meshes grid resolution of 5123, and simplify them to 50000 faces via decimation. Since
7

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Input Image
Ours
HoloPart
Figure 5:
Comparison Part-level 3D Generation. Our method directly generate complete parts,
while other methods require mesh segmentation and part completion.
Single Volume
Dual Volumes
Method
Hunyuan3D-2 [59]
Hi3DGen [54]
TripoSG [22]
Ourst
Ours
ULIP [48]
0.1609
0,1641
0.1726
0.1729
0.1715
ULIP-2 [49]
0.3962
0.3944
0.4048
0.4022
0.3986
Uni3D [61]
0.3872
0.3864
0.3912
0.3941
0.3906
Table 1: Comparison of image-to-3D generation. We measure the cosine similarity between generated meshes and input images using different feature extractors. Our model pretrained on fused shapes to initialize our part-level model.
both ULIP-2 and Uni3D require colored point clouds as input, we assign a uniform white color to all mesh outputs before computing the metrics. As shown in Table 1, our model pretrained on fused shapes achieves competitive performance, aligning well with qualitative visual results. While the part-level fine-tuned model yields slightly lower scores due to its structural decomposition focus, remains comparable to recent approaches, demonstrating a favorable trade-off' between condition following and part-aware generation.
We further compare inference speed the context of pert- generation. Specifically, HoloPart [52] requires a segmented mesh as input, which depends on both an image-to-3D model and mesh segmentation model [39, 51, 28]. These two preprocessing steps alone take several minutes, and the subsequent part- wise completion time increases linearly with the number of parts. In contrast, our method takes a single-view image and directly outputs 3D parts in about 30 seconds no matter how many parts it contains, achieving huge acceleration.

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Figure 6: Diversity on Image-to-3D Generation. We can generate diverse and meaningful parts with different random seeds.
w/o P-VAE
w/ P-VAE
InputImage Image
w/o Data Cleaning
w/ Data Cleaning
Figure 7: Ablation Study. We ablate different training designs and compare the generation quality,
4.4 Ablation Study
We conduct ablation studies on key design choices in the part-level finetuning stage, as shown in Figure 7. First, we evaluate the impact of part-level finetuning on the VAE (denoted as P-VAE). The VAE is originally pretrained on fused shapes that are normalized and recentered; when directly applied to part-level data, which may not be centered, it often introduces artifacts. After finetuning on lert- data, the VAE becomes more robust to such spatial variations. Second, we assess the effect of the data filtering process. Without filtering, the model tends to generate overly simplistic segmentations (often consisting of only two parts), whereas filtering the training data leads to improved segmentation quality and greater diversity. Lastly, we also validate the necessity of the part embedding. We find that without adding this learnable embedding the second latent code, the model struggles to differentiate between the dual volumes and generates overlapping or identical parts.
4.5 Limitations and Future Work
Our method still exhibits several notable limitations: (1) Limited control over part granularity. The part extraction process relies solely on scene graph information from the input meshes, which are often noisy or inconsistent across the dataset. As a result, the extracted part divisions are diverse but unpredictable, hindering both consistency and user control. Incorporating additional input such as segmentation mask may offer improved part-level controllability. (2) Constraints of bipartite contraction and dual-volume packing. While these strategies serve as practical solutions for managing complex part relationships, they are inherently limited in expressive capacity. For instance, three mutually contacting parts cannot be represented using only two volumes, leading to suboptimal results. A potential solution is to allow for more volumes, such as converting the connectivity graph into planar graph and applying the four-color theorem [36] for packing. These limitations still constrain the model's capacity for precise editing and joint articulation of any 3D objects.

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5 Conclusion
In this paper, we presented novel end-to-end framework for part-level 3D generation from single image. By leveraging 3D part connectivity and introducing a dual volume packing strategy, our method avoids reliance 2D segmentation priors and efficiently handles an arbitrary number of parts. Experimental results demonstrate that our method achieves high-quality, diverse, and efficient part-level mesh generation, outperforming existing baselines. This work offers a promising step toward editable and structured 3D content creation for downstream applications.
Acknowledgements. This work is supported by the National Key Research and Development Program of China (2020YFBI (ONDYYBB170002)) 708002), National Natural Science Foundation of China (61632003, 61375022, 61403005), Grant SCITLAB- A-070007 of Intelligent Terminal Key Laboratory of SiChuan Province, Beijing Advanced Innovation Center for Intelligent Robots and Systems (201 20081RS11) 1), and PEK-SenseTiime Joint Laboratory of Machine Vision.
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A More Implementation Details
A.1 Bipartite Contraction
We detail the greedy odd-cycle contraction algorithm used in the dual volume packing process in Algorithm I. The algorithm first performs depth-first search to identify all cycles (both even and odd) in the graph. It then iteratively processes each odd cycle by contracting the edge with the largest weight, repeating this step until no odd cycles remain. Figure 8, we present the distribution of the number of parts in our processed dataset. We observe that approximately 60% of the data samples contain fewer than 10 parts. Only 5% the samples have more than 200 parts. Since enumerating all simple cycles in graph is not a polynomial-time -immiilll-mmeeeeee operation and be time-consuming for graphs with dense edge structures, we restrict the use of this algorithm to graphs with fewer than 100 edges ensure processing efficiency. For more complex graphs, directly apply two-coloring and leave the conflicting edges where both vertices have the same color as contracted edges.
Algorithm 1: Greedy Odd Cycle Contraction
Data: Graph G {V,E), where [ M
Result: Array O to hold the edges to contract.
/* Find all cycles C (ci),, where each cycle C; {eg}
C DFS (g);
/* Loop and contract until there are no odd cycles. while C contains odd cycles:
for C:
if C is odd cycle:
Find the edge e with the largest weight in C.
arg max w(e');
/* Mark this edge for contraction.
O (ep::
/* Remove this edge from all cycles if exists.
for C" in C:
C" removvee(;:
return O;
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180
190
2200
Number of Parts
Figure 8: Statistics on the number of part in the processed dataset.
A.2 Surface Sampling
Through dual volume packing, we split the raw mesh into two sub-meshes. For each sub-mesh, we compute the unsigned distance field (UDF) on 512 grid. determine the empty region (positive distance), we apply flood-filling algorithm starting from corner voxel (e.g., the voxel at index [0, 0, 0)), which guaranteed to be outside the mesh since all meshes are normalized to the range [- 0.95, 0.95]3. We then define the complement as the occupied region (negative distance), yielding
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Input Image
TripoSG
DGen
Ours
Figure 9: More Comparisons Image-to-3D Generation.
signed distance field. Using Marching Cubes [29], we extract watertight surface from the resulting signed distance field.
Following Dora [5], we sample 32768 uniformly distributed surface points and 16384 salient edge points input to the VAF. The dihedral angle threshold for salient edge detection is set to less than 165". If no salient edges are present in the mesh, we randomly subsample from the uniform surface points serve salient edge samples. To supervise the signed distance function (SDF) output, further generate and store three types of point-SDF pairs: (I) Uniformly sampled points in the volume [1,,1] (2) Near-surface point samples; (3) dear-salient-edge point samples.
During VAE training, we randomly select 16384 uniform samples, 8192 nuar-surface samples, and 8192 sear-salient-ede samples in each iteration.
B
More Results
B.1
Qualitative Comparisons
provide more qualitative comparisons in Figure 9. Our method outperforms prior approaches in most cases, particularly in preserving fine-grained geometric details and producing complete
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X
Wrong Internal Structure
Unstable Segmentation
Figure 10: Limitations and Failure Cases. We showcase some failure cases of our model.
parts. Compared with baseline methods, our generated shapes exhibit better surface smoothness, part separation, and overall fidelity.
B.2
Failure Cases
In Figure 10, we show some typical failure cases of our model. The first type involves incorrect internal structures. For shapes that contain intricate internal components, the underlying part connec- tivity graph becomes highly entangled. In such cases, our greedy odd cycle contraction algorithm may fail to partition the mesh into two meaningful groups, resulting in incomplete incorrect reconstructions. For example, in some drawer-like objects, the inner compartment not properly connected to the outer casing, leading to broken missing internal parts. The second type of failure relates to unstable segmentation. Due to inconsistencies in part annotations within the training dataset and the absence of explicit part granularity control, our model results when given different random seeds. While some samples yield satisfactory and semantically aligned part divisions, others may result in undesirable fusions or erroneous splits. For instance, the wheels car may sometimes be merged with the car body, violating the expected part separation. This instability highlights the need for more reliable part annotations and stronger priors enforce consistent structural decomposition.
produce varying segmentation
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Reviews:
[{"review_id": "36PUfz5oP1", "reviewer": "Reviewer_5MKr", "summary": "The paper presents an end-to-end framework for generating part-level 3D objects from single images using a dual volume packing strategy. The key idea is converting part-connectivity graphs into bipartite graphs, allowing all parts to be packed into two volumes where non-contacting parts can be naturally separated. The method extends the existing 3D generation model to simultaneously generate two latent codes that decode to two volumes. The experiments shows visually better segmentation compared to prior methods.", "questions": "Please see weakness above.\n\nWhile I appreciate the technical novelty of the dual volume packing strategy for addressing efficiency in part-level 3D generation, I have concerns about the method's broader impact and extensibility. The efficiency gains, though good, may not address the most critical challenges in this domain. More pressing issues include faithfull reconstruction of input details, consistent part generation, and geometric constraints between parts - areas where this method shows limited promise for improvement.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. The dual volume packing strategy that converts 3D shapes into two separate volumes containing disjoint parts is well-motivated and enables efficient generation. The approach is solid and produces convincing results.\n2. The dataset processing pipeline with part extraction, repair and filtering is important for the community. Open-sourcing this pipeline would benefit future research in part-level 3D generation.\n3. The presented results demonstrate visually appealing part-level 3D generation with reasonable separation and overall shape.\n\nWeakness:\n\n1. The part divisions generated depend entirely on the model's learned representations from training data, and the presented representation has no mechganism for consistency guarantee. For example, in the Figure 4 row 2 example, the method fails to faithfully generate the four eyelashes visible in the input image. This limitation cannot be easily addressed by the segmentation mask approach mentioned in the authors' limitation discussion.\n2.  The method lacks mechanisms to enforce geometric and topological constraints between parts. The required post-processing steps further reduce user control over the final output and limit the method's applicability for precision applications.\n3. The paper would benefit from additional examples in the image-to-3D comparison beyond those currently presented. More diverse test cases would better demonstrate the method's capabilities and limitations.\n4. On the comparison experiment, we still don't know how much the performance difference is because of the training data, since there is no experiment training the prior method on the new data or vice-versa. \n\nMinor Weakness:\n\n5. While not strictly necessary, developing metrics specifically for part-level 3D generation quality, consistency, and semantic correctness would strengthen the evaluation. Current comparisons rely on general generation quality metrics, and part-specific metrics would benefit future research and enable more meaningful comparisons in this domain.", "was_revised": true, "final_justification": "The authors addressed my concerns. I tend to keep my current score and lean toward accepting the paper.", "rebuttal": "Thank you for your valuable time and insightful comments! We have tried to address your concerns in our rebuttal:\n\n**Q: The part divisions generated depend entirely on the model's learned representations from training data, and the presented representation has no mechanism for consistency guarantee.**\n\nWe acknowledge that our data-driven part division approach has limitations, especially when the dataset is of mixed quality. As illustrated by the Sponge Bob example, even whole-shape baselines struggle to generate fine details such as the four eyelashes. Given that our part-packing strategy is model-agnostic, we anticipate improvements in part consistency and quality with better backbones and higher-quality datasets.\n\n**Q:  Lack of mechanisms to enforce geometric and topological constraints between parts.**\n\nSimilar to the previous point, our model primarily relies on dataset-provided part information to learn accurate geometric and topological relationships. Although the model uses attention layers to exchange information between different parts implicitly, the generated parts may still be inconsistent or suboptimal with certain random seeds.\n\n**Q:  More test cases and comparisons to better demonstrate the applicability of the model.**\n\nThank you for this suggestion! We will create a comprehensive project page featuring additional samples and provide open-source code with a demo to allow users thorough testing of our model.\n\n**Q:  Influence of training data on the comparisons.**\n\nDue to our part-related filtering and preprocessing steps, our dataset contains approximately 254K samples, smaller than TripoSG (2M samples including private datasets) or Hi3DGen (870K including synthetic samples). However, since most baselines majorly rely on subsets of Objaverse and Objaverse-XL for training, it's still relatively fair to compare with.\n\n**Q:  Metrics for part-level 3D generation quality, consistency and semantic correctness.**\n\nThank you for this suggestion! Currently, no standard metrics exist for evaluating part-level generation quality in 3D generation. Developing robust, automated metrics in this domain alone remains a difficult research challenge, which is why we still rely predominantly on qualitative assessments and will note this limitation in the revised manuscript."}, {"review_id": "3KFFgyF58Z", "reviewer": "Reviewer_7qd5", "summary": "This paper proposes a new 3D part generation method based on Dual Volume Packing. The authors first extract a bipartite graph from the 3D mesh to encode dual volume latents. A flow model is then employed to generate these latents. The authors compare their proposed method with state-of-the-art 3D generators.", "questions": "See the weaknesses.", "limitations": "Yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. The proposed Dual Volume Packing approach is novel and reasonable. The dual volume latents can support a large number of parts, as all parts are divided into two volumes.\n\n2. The proposed method demonstrates comparable global results with state-of-the-art 3D generators while achieving effective part decomposition.\n\n3. The presentation is clear and easy to follow.\n\nWeaknesses:\n\n1. To extract all meshes in the dataset into a bipartite graph, the authors propose a sequence of edge contraction operations. However, these operations require merging parts, which may compromise the original part information in the mesh.\n\n2. The authors do not provide an analysis of part consistency, as all parts within one volume are disconnected. I have concerns about the consistency of combinations, particularly regarding mesh penetration issues when parts from the two volumes are merged.\n\n3. Minor issue: the current framework does not generate texture.", "was_revised": true, "final_justification": "My concerns are addressed. I tend to keep my score.", "rebuttal": "Thank you for your valuable time and insightful comments! We have tried to address your concerns in our rebuttal:\n\n**Q:  The part-merging in data preprocessing may compromise the original part information.**\n\nWe strive to preserve original part annotations in our data pipeline and only merge parts when absolutely necessary. Specifically, when GLB meshes provide meta-information regarding geometry groups, we directly use these annotations. Otherwise, we fallback to treating each connected component as an individual part. However, we observed many issues caused by operations like UV-unwrapping, where surfaces of the same semantic part are split for texturing ease. Therefore, empirical part-merging rules are applied to recombine these disconnected surfaces appropriately.\n\n**Q:  Lack of analysis on part consistency when parts from two volumes are merged.**\n\nWe consider minor overlaps or penetrations between parts acceptable, as they frequently appear in artist-created meshes. Our approach effectively reproduces such behavior, as demonstrated in Figure 5 and supplementary videos. Even when overlaps occur, each generated part remains individually complete and watertight, since they are generated independently in separate volumes.\n\n**Q:  No support of texture generation.**\n\nThank you for your suggestion! Similar to other 3D-native diffusion models (e.g., CLAY, TripoSG, Hi3DGen), we currently focus on improving geometry generation quality. Texture model typically leverages a distinct 2D multi-view diffusion pipeline, differing significantly from the geometry model. Therefore, we consider texture generation outside the immediate scope of this work."}, {"review_id": "47FASEtDZK", "reviewer": "Reviewer_42Py", "summary": "This paper proposes a novel end-to-end framework for generating high-quality 3D objects with an arbitrary number of semantically meaningful parts from a single input image. The key innovation lies in the dual volume packing strategy, which allows the parallel generation of high-quality 3D objects with an arbitrary number of complete and semantically meaningful parts, improving efficiency and robustness. Sepecificaly, the authors introduce a heuristic algorithm to transform part connectivity graphs into bipartite graphs, enabling efficient packing of parts into two complementary volumes. This method demonstrates superior performance in terms of part-level and object-level generation.", "questions": "- **Choice of Volume Representation**: The paper utilizes a volume-based representation for 3D object generation, which, while effective, is known for its high computational and storage costs, especially as resolution increases. Given these limitations, have the authors considered alternative representations, such as implicit representations like NeRF or Gaussian fields? If so, what were the reasons for ultimately choosing the volume-based approach, and are there any plans to explore these alternatives in future work?\n\n- **Evaluation Metrics**:The current evaluation relies heavily on cosine similarity-based metrics (ULIP, Uni3D), which primarily assess the alignment between generated meshes and reference images. While these metrics are useful, they do not provide a comprehensive evaluation of 3D generation quality. Have the authors considered incorporating additional metrics commonly used in 3D generation, such as GPTEval3D, user studies, or 3DGen-Score? These metrics could offer a more holistic view of the method's performance, including aspects like geometry plausibility, geometry details.\n\n- **Downstream Application Details**:The teaser image showcases visualizations of downstream applications, but the paper lacks a detailed description of these experiments. Could the authors provide more information on the setup and results of these downstream tasks?  And what benefits did the proposed method bring compared to existing solutions?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "**Strengths**\n\n- **Quality**： The paper is technically sound. The dual volume packing strategy effectively addresses the challenge of handling overlapping parts in 3D objects.  The results demonstrate superior performance compared to existing methods.\n- **Clarity**：The paper is well-organized and clearly written. The authors provide detailed descriptions of their approach and experimental results. \n- **Originality and Significance**： The dual volume packing strategy is a novel contribution. And the ability to generate part-level 3D meshes directly from images without relying on segmentation priors is a significant advancement. \n\n**Weaknesses**\n\n- **Evaluation Metrics**: The quantitative analysis relies solely on the cosine similarity between 3D and 2D features. While this metric effectively assesses the similarity between the generated meshes and reference images, it does not fully capture the quality of the 3D generation, such as shape fidelity, part completeness, or structural consistency.  A more comprehensive evaluation is expected.\n\n- **Downstream Applications**:The paper provides limited discussion on the downstream applications of the proposed method. While the teaser posts potential applications in *Joint Articulation*  and *Editing*, there is no detailed exploration of how the method could be integrated into these fields or what specific benefits it would bring.", "was_revised": true, "final_justification": "My concerns have been addressed. I will keep my score as Accept.", "rebuttal": "Thank you for your valuable time and insightful comments! We have tried to address your concerns in our rebuttal:\n\n**Q:  Lack of evaluation on shape fidelity, part completeness or structural consistency.**\n\nQuantitative evaluation of 3D generation, especially at the part-level, remains very challenging. Current rendering-based benchmarks (GPTEval3D, 3DGen-Bench) are optimized for appearance rather than geometry, which is not the focus of our method. Also, due to limited part-wise evaluation methods and the complexity of user studies in this context, we emphasize qualitative evaluation. To encourage future evaluation advancements, we will release our code and a demo to facilitate comparisons.\n\n**Q:  Limited discussion on the downstream applications such as joint articulation and editing.**\n\nThank you for highlighting this issue! Downstream tasks like articulation and editing are indeed primary motivations for our method. However, our current model, although promising, still exhibits robustness limitations, as evidenced by failure cases in Figure 3 of the supplementary materials. These limitations stem from mixed-quality datasets and the two-volume part-packing approach. We will clarify this limitation explicitly in the revised manuscript to prevent any potential overclaims.\n\n**Q:  Choice of volume generation compared with alternatives such as implicit NeRF or GS.**\n\nWe adopted volume-based latent diffusion mainly following recent advancements (e.g., Shape2Vecset, CLAY, TripoSG). Regarding computational cost, since the diffusion model is trained on the compact latent space, it's not significantly different from other representations, and avoids rendering-based limitations (e.g., occlusion) of NeRF and GS. Nevertheless, our core part-packing concept is representation-agnostic, allowing flexibility in adopting alternative representations if advantageous."}, {"review_id": "vk7fD7j200", "reviewer": "Reviewer_Gc4w", "summary": "This paper proposes a novel end-to-end framework for part-level 3D object generation from\na single image. Unlike previous approaches that generate fused meshes or require external\nsegmentation priors, the method directly predicts multiple complete and semantically\nmeaningful parts. The core contribution is a dual-volume packing strategy, which organizes\nparts into two separate volumes based on their spatial connectivity. This bipartite-based\npacking allows the model to efficiently handle arbitrary numbers of disjoint or contacting\nparts while maintaining structural separation. The framework is trained using a large-scale\ncurated dataset derived from Objaverse-XL, with extensive preprocessing to ensure\npart-level consistency and watertight geometry. The model leverages a VAE and a rectified\nflow-based denoising module conditioned on image features extracted by DINOv2.\nExperimental results demonstrate that the proposed method outperforms prior baselines in\nvisual quality and structural coherence, offering diverse, editable 3D outputs suitable for\ndownstream applications such as content creation or robotics.", "questions": "Given that the method generates 3D objects from a single input image, could the authors\nclarify if and how the approach addresses the reconstruction or completion of unseen or\noccluded parts of the object? Understanding this aspect is important to assess the model’s\ncapability for generating complete and realistic 3D shapes from limited views.", "limitations": "Since the number of parts is not provided as an input, the resulting segmentations may not\nreflect the user’s desired level of granularity. This limits user control and reduces the\nconsistency and predictability of the output.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths:\nThe proposed framework directly predicts complete and semantically meaningful 3D\nparts from a single image, without relying on 2D/3D segmentation priors. This marks\na clear advancement over prior methods that depend on fused meshes or\nsegmentation-based pipelines.\n\nThe introduction of a dual volume packing strategy, where the part-connectivity graph\nis transformed into a bipartite structure via heuristic edge contraction, is technically\nnovel and practically effective. It enables efficient packing of disjoint parts while\npreserving structural separation, addressing a key challenge in part-level generation.\n\nAlthough the training phase is resource-intensive, the inference process is efficient\nand scales independently of the number of parts, an advantage over sequential\npart-wise methods.\n\nWeaknesses:\nCompared to existing models, our method requires substantially more computational\nresources. For example, TripoSG takes about 3 weeks of training on 160 A100\nGPUs, and Hi3DGen uses only 8 NVIDIA A800 GPUs, whereas this paper’s\napproach demands up to 256 A100 GPUs over multiple weeks. This high cost may\nlimit practical adoption and accessibility.\n\nThe ablation study presented in Figure 7 primarily shows qualitative comparisons\nsuch as “/ w/o P-VAE / w/ P-VAE” and “w/o Data Cleaning / w/ Data Cleaning”\nfocusing on generation quality. However, the paper lacks quantitative evaluation\nmetrics for these design choices.\n\nAdditionally, even for the same object category (e.g., a tree), the model exhibits\ninconsistent behavior depending on the scale: larger-scale instances tend to be\nrepresented as a single part, while smaller-scale instances are segmented into\nmultiple parts such as individual leaves.\n\nThe paper includes joint articulation and editing as visual examples in Figure 1,\ndespite not implementing or evaluating these capabilities in the proposed model.\nWhile these applications may serve as motivation, their inclusion without empirical\nsupport or clarification may mislead readers into assuming that the method supports\nsuch functionalities. This weakens the clarity and transparency of the paper’s scope\nand contributions.\n\nWhile not strictly necessary for this kind of paper, a user study or at least user-centric\nevaluation would have strengthened the claims around usability  and editability,\nespecially since interactive applications are a key motivation.", "was_revised": true, "final_justification": "The author addressed my concerns well, so I will raise my judgment.", "rebuttal": "Thank you for your valuable time and insightful comments! We have tried to address your concerns in our rebuttal:\n\n**Q:  The method requires more computational resources.**\n\nWe respectfully disagree. Our VectorSet-based 3D diffusion model’s computational requirements (256 A100 GPUs for 2 weeks) align closely with comparable models like CLAY (256 A800 GPUs for 15 days) and TripoSG (160 A100 GPUs for 3 weeks). Hi3DGen benefits from fine-tuning the efficient sparse volume latent representation of TRELLIS. Furthermore, since the model is progressively trained in multiple stages similar to CLAY, extended training yields diminishing returns and allows for a trade-off between training duration and performance.\n\n**Q:  Lack of quantitative evaluation and ablations for design choices.**\n\nThank you for the suggestion! Quantitative evaluation remains challenging for 3D generation tasks. Furthermore, existing metrics primarily target full-shape appearance rather than part-level geometry. Given this limitation, we mainly rely on qualitative ablation studies to demonstrate design impacts visually, especially the noticeable improvements after fine-tuning the VAE.\n\n**Q:  Inconsistent part divisions and limited controllability.**\n\nWe acknowledge this major limitation, illustrated in Figure 3 of the supplementary materials. This inconsistency largely arises from mixed-quality data, where objects within the same category often exhibit varying part divisions due to different workflows or styles of the artists. We have discussed potential future directions, such as incorporating 2D segmentation masks (e.g., from SAM) to guide more consistent and controllable 3D part division.\n\n**Q:  Lack of clarification on downstream tasks such as joint articulation and editing.**\n\nThank you for highlighting this issue! Downstream tasks like articulation and editing are indeed primary motivations for our method. However, our current model, although promising, still exhibits robustness limitations, as evidenced by failure cases in Figure 3 of the supplementary materials. These limitations stem from mixed-quality datasets and the two-volume part-packing approach. We will clarify this limitation explicitly in the revised manuscript to prevent any potential overclaims.\n\n**Q:  How to generate unseen or occluded parts of the object?**\n\nOur model follows recent advances in 3D native diffusion models (e.g., CLAY, TripoSG, Hunyuan3D, TRELLIS), which first learn compact 3D latent representations from complete 3D shapes, and then train a diffusion/flow-matching model to map input image features to the latent space. Therefore, the generated 3D shapes are mostly complete. This solves some occlusion problems, for example, even if one chair's leg is unseen in the image, we can correctly predict all four legs based on the semantic clues. Our part-based model further allows for predicting each leg separately. However, the model may not be able to predict totally out-of-domain shapes due to the data-driven nature."}]

Author rebuttal:

Meta-review: This paper proposes a method for part-based 3D shape generation from a single image. It has received all positive reviews.

The paper was found well organized, clear, easy to follow. The method was found well motivated, novel, solid, technically sound, efficient, important for the community. The results were found convincing, comparable or superior to state-of-the-art, visually appealing,

Concerns included the lack of consistency guarantee originating from the segmentation mask approach and the lack of relevant analysis, the requirement to merge parts, the lack of mechanisms to enforce geometric and topological constraints between parts, the insufficient examples, the insufficient metrics, the lack of ablation on training data, the limited discussion, the high training cost, the dependence on scale, the inclusion of visual examples on functionalities without empirical support, the lack of a user study.

The authors provided detailed feedback in their rebuttal. This feedback addressed all reviewers' concerns or it was acknowledged that there are limitations to be discussed in the paper, which is still okay. The authors committed to update the paper according to the new material they have presented during the discussion.

It is therefore recommended to accept the paper.
Decision: Accept (poster)