Title: Efficient Part-level 3D Object Generation via Dual Volume Packing
Venue: NeurIPS 2025
Accepted: yes
Keywords: 3D Generation

Part Generation

Image-to-3D
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/}.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nGiven 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.\n\nLIMITATIONS:\nSince 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\n- **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?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-01"}

comments: 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.

STRENGTHS AND WEAKNESSES:
Strengths:

1. 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.

2. The proposed method demonstrates comparable global results with state-of-the-art 3D generators while achieving effective part decomposition.

3. The presentation is clear and easy to follow.

Weaknesses:

1. 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.

2. 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.

3. Minor issue: the current framework does not generate texture.

QUESTIONS:
See the weaknesses.

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-03

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nPlease 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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}