DIPO: Dual-State Images Controlled Articulated Object Generation Powered by Diverse Data
Authors: Ruiqi Wu
Xinjie wang
Liu.Liu
Chun-Le Guo
Jiaxiong Qiu
Chongyi Li
Lichao Huang
Zhizhong Su
Ming-Ming Cheng
Venue: NeurIPS 2025 poster ()
Keywords: articulated object
articulated object dataset
Primary area: applications

Abstract: We present **DIPO**, a novel framework for the controllable generation of articulated 3D objects from a pair of images: one depicting the object in a resting state and the other in an articulated state.
Compared to the single-image approach, our dual-image input imposes only a modest overhead for data collection, but at the same time provides important motion information, which is a reliable guide for predicting kinematic relationships between parts.
Specifically, we propose a dual-image diffusion model that captures relationships between the image pair to generate part layouts and joint parameters. In addition, we introduce a Chain-of-Thought (CoT) based **graph reasoner** that explicitly infers part connectivity relationships.
To further improve robustness and generalization on complex articulated objects, we develop a fully automated dataset expansion pipeline, name **LEGO-Art**, that enriches the diversity and complexity of PartNet-Mobility dataset. We propose **PM-X**, a large-scale dataset of complex articulated 3D objects, accompanied by rendered images, URDF annotations, and textual descriptions.
Extensive experiments demonstrate that DIPO significantly outperforms existing baselines in both the resting state and the articulated state, while the proposed PM-X dataset further enhances generalization to diverse and structurally complex articulated objects.
Our code and dataset are available at https://github.com/RQ-Wu/DIPO.

DIPO: Dual-State Images Controlled Articulated Object Generation Powered by Diverse Data
Ruiqi Wu Xinjie Wang Liu Liu Chunle Guo
Jiaxiong Qiu Chongyi Li
Lichao Huang
Zhizhong Su
Ming-Ming Cheng Horizon Robotics
NKKIRI,, Shenzhen Futian
VVCIP CS, Nankai University
Abstract
We present DIPO, a novel framework for the controllable generation of articulated 3D objects from a pair of images: one depicting the object in a resting state and the other in an articulated state. Compared to the single-iman approach, our dual- image input imposes only a modest overhead for data collection, but at the same time provides important motion information, which is reliable guide for predict- ing kinematic relationships between parts. Specifically, we propose a dual-image diffusion model that captures relationships between the image pair to generate part layouts and joint parameters. In addition, we introduce a Chain-of- Chain-of-Thought (CoT) based graph reasoner that explicitly infers part connectivity relationships. To further improve robustness and generalization on complex articulated objects, develop fully automated dataset expansion pipeline, name LEGO-Art, that enriches the diversity and complexity of PartNet-Mobillty dataset. We propose PM-X, a large-scale scale dataset of complex articulated 3D objects, accompanied by rendered images, URDF annotations, and textual descriptions. Extensive experi- ments demonstrate that DIPO significantly outperforms existing baselines in both the resting state and the articulated state, while the proposed PM-X dataset further enhances generalization to diverse and structurally complex articulated objects. Our code and dataset are available at https:/githbb.co//RWWWDD
Introduction
Articulated objects are pervasive in everyday environments. Achieving accurate modeling of articu- lated structures is the key enabler for building interactive virtual environments. plays a crucial in
simulation [5] 44 48), animation [50 5 33 21), robot manipulation [1] 9
28
32
and embodied
AI
19 36
31
20
However, constructing such models manually is highly labor-intensive and unscalable. As re- sult, increasing attention has been devoted to developing automatic methods for articulated object Despite promising progress, existing methods exhibit clear performance degradation when applied to structurally complex or visually ambiguous objects. These limitations stem from two fundamental bottlenecks.
modeling [1 22
45
18
24
6 23
The first issue input modality constraints. Reconstruution--aase approaches [4] 45 often rely on multi-view or multi-state images to reconstruct articulation behavior with high accuracy, While effective, these methods demand expensive data acquisition setups, precise camera calibration, and well-aligned temporal input, making them difficult to scale. On the other research line, benefiting from the controllability of diffusion models I2
38 39], many generation- based methods [18 24 6 23 are proposed. They utilize minimal input, such as category priors
35 30 47
"This work was done while Ruiqi Wu was Research Intern with Horizon Robotics. t denotes correspondence author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

Articulated State
Articulated State
Singapo
(b) DIPO (Ours)
Figure 1:
Visual comparison on real-captured data. (a) SINGAPO struggles with challenging
data and fails model motion relationships due its reliance on single input. However, our DIPO (b), which conditioned on dual-state image pairs, effectively generates accurate layouts and enables precise control if part motion across different articulated states.
or a single RGB image, to synthesize articulated objects directly. However, category priors lack spatial specificity, and single-image inputs lack of explicit articulation information. As result, these methods can only infer kinematic behaviors in a probabilistic manner. Consequently, neither class of methods offers both control and generalization ability when facing challenging data.
Secondly, limitations in training data. Data-driven modeling approaches require large-scale datasets with both articulation diversity and structural complexity. However, most existing datasets fall short in some aspects. For example, PartNet-Mobiilty (PM) [48] offers a large number of articulated assets, but the object instances dominated by simple and repetitive layouts with limited variability. In contrast, the Articulated Container Dataset (ACD) [14] contains more realistic and structurally diverse objects, but suffers from small scale, limiting its utility for model training.
To address the first issuse, we propose DIPO, a generation framework for 3D articulated objects conditioned on resting (closed) state and articulated (open) state image pairs. The dual-state image pair encodes essential motion cues and connectivity information. Compared to single-image methods, dual-state input resolves ambiguity in part motion and spatial relationships. As for multi-view methods, it is significantly easier to acquire while maintaining sufficient articulation information. DIPO is built on diffusion transformer architecture [30] and consists of two components. First, "Dual-State Injection Module helps the network to model the relationships between dual state images. Second, Graph Reasoner based Chain-of-Thought (Co'T) techniques 17 infers part connectivity step by step. Moreover, this module few-shot learns on visual prompts synthesized by GPT-4o 1 acquire better performance. The proposed method achieves higher controllability and improved performance in articulated 3D object generation.
dataset named CoitNet-Mobblity-Conee (PM-X), which provides diverse and structurally complex articulated objects with rendered images, URDF annotations [34] and language descriptions. PM-X is built by fully automated data construc- tion pipeline based on an agent system, named LEGO-Art. Starting from natural language prompts sampled from LLM 3), the pipeline first generates coarse part layouts in discretized 3D space. Then we develop a toolkit to transfer them to annotations with precise coordinates and articulation parameters. Based retrieval algorithms (24). we can acquire the final 3DD object and the rendered images. Finally, a vision-language model (VLM) 1 is used to filter implausible samples.
In response the second challenge, propose
We collect resting state image from the Internet and generate corresponding articulated state images by a visual generative model [] As illustrated in Figure [ our method outperforms the state-of-the-art method, i.e. SINGAPO 23 Our main contributions are summarized as follows:
We propose a novel dual-state image model for controllable articulated 3D object generation, integrating layout diffusion and CoT-based connectivity reasoning.

---

We develop LEGO-Art pipeline to construct structurally diverse articulated objects, and contribute PM-X, a new large-scale dataset with rendered images and physical annotations.
Extensive experiments demonstrate that DIPO significantly outperforms state-of-the-art methods, and the proposed LEGO-Art and constructed PM-X dataset enhance generalization complex structures.
2 Related Work
2.1 Articulated Object Creation
Recent progress in articulated object modeling can be broadly categorized into reconstruction-bass and generation-based approaches.
Reconstruction methods commonly rely multi-view or multi-state inputs to reconstruct part-level geometry and articulation parameters. CLA-NeRF [4]] reconstructs articulated objects from sparse multi-view RGB images within known category. PARIS [22] extends this setting to unknown categories with dual-state multi-view RGB images. Weng et al. [45] further incorporate depth information to support richer geometry priors. However, they rely densely aligned inputs and known part counts, limiting their applicability in real-world settings. In contrast, our approach only conditioned a pair of images, which reduces input complexity while preserving articulation fidelity.
Generative approaches aim to synthesize articulated objects from compact inputs, bypassing the need for dense observations. NAP [18) parses layouts and articulation parameters into graphs and generates articulated 3D objects unconditionally. CAGE (24) achieves a controllable generation from the given articulation graph. Despite these models support efficient sampling, they lack explicit visual guidance to achieve more accurate controllability. URDFormer [6] solves this issue by combining a
visual detector 225 46 to extract spatial layout and a transformer to predict articulation parameters.
SINGAPO [23] proposes a diffusion model 12 37 35 30 conditioned on resting state images to generate articulated objects. However, the controllability of current approaches remains limited due to the absence of explicit articulation dynamics. The proposed DIPO effectively addresses this limitation by utilizing the motion information provided by pair of images captured in the resting and articulated states.
2.2 Synthetic Articulated Object Datasets
The availability of large-scale 3D datasets with part-level structures has significantly facilitated research on articulated object modeling. Early datasets such as those used in [3] 49 are constructed by manually segmenting shapes from ShapeNet [4] and SketchUp [40], and annotating articulation parameters for part pairs. Shape2Motion 422 expands the scale by introducing an annotation tool that supports visual verification through animation. PartNet-Mobility [48 is large-scale articulated object dataset constructed on [2) It offers annotations of part-level articulation along with high-quality rendered images, and is one of the most widely adopted benchmarks. GAPartNet [TO) focuses on functional part detection across categories, emphasizing generalizable and actionable parts such as buttons and handles. These datasets have enabled the development of deep learning models for articulation analysis, but are still limited in structural complexity and diversity. To improve articulation diversity and realism, ACD [44] collects complex articulated objects from ABO [7]. 3D-Future [8] and HSSD [15]. While the articulation structures in ACD are more intricate. the scale of dataset remains limited. To address both diversity and scalability limitations. we present PM-X, a large-scale, URDF- compatile dataset of procedurally generated articulated objects with high structural complexity.
3 Generate Articulated Objects from Dual-Image Pairs
3.1 Overview
We propose a diffusion network to generate all the parameters of articulated objects conditioned on a pair of dual-state images and pert-level connectivity graph. The overall architecture is illustrated Figure To support this generation process, we parameterize each part terms of its spatial location, articulation connectivity, and semantic attributes. The i-th part p, is represented by the
3

---

Image
Taansfemer Logur
Purtiet Mobulty
Czoss Atta
Graph
20
Vissal
Glocal Attn
Ratseval
Grapa
Procppts
Looil
Our PM-X data
Trunfoemer Layes
Prinitivss
Aszembble
Artictiio Graph
Adjaccncy Matri
LEGO-Ait
Artoulieed Object
Figure 2: Overview of the proposed DIPO framework. The left part shows the proposed LEGO-Art pipeline assembles the primitives in existing dataset and construct the PM-X dataset, which are more diverse and complex compared to PM dataset. The right part shows that our diffusion model equipped with CoT-based Graph Reasoner for articulate graph inference, and conditioned on resting & &rticulated image pairs to generate articulated objects.
bounding box coordinates b; E R semantic label 4, articulation type é, joint axis a; E R and motion range E IR?. To facilitate unified processing, all attributes repeated to 6-dimensional array, resulting in 5 x matrix representation for each part.
3.2 Dual-State Image Conditioning
We condition the denoising process on both resting- and articulated-state images capture motion- aware cues. Let FR and FA denote the DI- NOv2 [29] features from the resting and articulated images, respectively. To integrate these into the dif- fusion network, we apply a Dual-State Injection Module at each layer.
Given part embeddings X, first perform attention with resting-state features FR to capture static appearance. We then guide articulated fea- tures FA to attend to FR. and subsequently inject this context-enhanced signal into X. The overall conditioning update at each diffusion step is defined as:
(a) Resting state
(b) Articulated state
Figure 3: Dual-state visual prompt used by the Graph Reasoner. GPT-4o can produce realistic and structurally complex image pairs.
X CAF
(1)
where CA(Q, K') denotes standard cross-attention operation that query Q attends to key-value source K. This design allows the model generate more accurate part movement and joint behavior by contrasting the two input states.
3.3 Graph Reasoner via Chain-of-Thought Prompting
We introduce the Graph Reasoner, Chain-of-Thoune (CoT) based module that predicts the articulated part connectivity graph from dual-state images, serving as a structural prior for the diffusion process. The reasoning follows a step-by-step paradigm. It first identifies candidate parts and estimates their coarse spatial layout, then verifies whether the layout satisfies the given articulation rules, and finally infers attachment relationships to generate the articulation graph. After that, convert the predicted articulation graph into an adjacency matrix, which serves as an attention mask guide the self-attention of the diffusion model along valid structural connections.
In addition, we leverage the fnstroction-folloiiing and visual-editing capabilities of GPT-4o to generate dual-state image pairs of structurally diverse objects Figure 3 shows. These results

---

AI Agont Desióner
Desoriptionn Roller
Layovtt Buildor
Soripting Toolkit
'Retrioval 'Rondor
Visual Filter
Description
Grid Occapancy
Predise Coordinate
Remdered Result
Varification
A storage furniture with top, three 0.51,
grte 6,
Inmmee 'door",
Analysis:
grdcly:
"centor::
1.Drowen
[3.2,
[-0294, 0.254, 0.217). 'áea":
2)
(c. 0.794, n.00s) "Joins":1
Top Benavior
drawers
"door",
niddle, and one drawer at the
Joint:
type: "ravolute",
"hype) "revoutee
3.Handle Placerent
hinge: "left",
"range" J0, -90).
'ongin"
4.Notions
1855) "direction"
Final Verdiet: Yeu
Figure 4: An overview of the fully automated synthesis pipeline for the proposed PM-X dataset. The synthesis pipeline consists of five functional modules executed sequence: (1) a description roller that uses an LLM to generate natural language descriptions for structured layout, (2) layout builder to generate part-level grid occupancy and joint configurations, (3) a scripting toolkit to construct precise coordinate from the grid-based layout information, (4) retrieval and render module to assemble geometry and render dual-state images, and (5) visual filter that uses a VLM to validate the plausibility of generated samples. In particular, modules (1), (2), and (5) automatically constructed and managed by the Al Agent Designer.
serve as strong example visual prompts for the Graph Reasoner to achieve a higher stability and generalization of graph prediction.
4 Construct Complex Data from Partnet-Mobility
4.1 LEGO-Art pipeline
To gain favorable performance on challenging data, we require a large-scale 3D dataset with diverse part layouts. However, existing datasets still fall short in complementary ways: PM [48) offers sufficient data lacks articulation complexity, while ACD [14] includes more realistic kinematic structures but is limited in dataset scale.
To address this issue, we design a Language-driver Engine via Grid Organization for Articulation objects construction (LEGO-Art). It is fully automated synthesis pipeline that generates complex articulated 3D assets by assembling part primitives from existing dataset. Figure shows the overall workflow of the synthesis pipeline. The details of each step are illustrated below.
Description Roller. The pipeline begins by generating natural language description of an articulated object by LLM agent (e.g.. "a storage furniture with two doors on top, three drawers in the middle, and one drawer at the bottom"), This serves as a high-level blueprint for the object's structure without requiring precise geometry.
Layout Builder. Given this textual input, the second agent translates the description into part layout and articulation configuration. Instead of predicting exact 3D coordinates. which often introduces hallucination, we discretize the space into grid and assign parts to grid cells. Each part is associated with joint metadata such as type, axis, and motion direction.
Scripting Toolkit. We develop scripting toolkit that converts the grid-level spatial layout into precise 3D coordinates and assigns articulation parameters of the axis and direction of joint, motion range and joint type.
Retrieval Render. We assign geometry to each part by retrieving mesh primitives from PartNet-Mobille by the algorithm proposed by [24] Parts are scaled and positioned
5

---

according to the layout, and connected as specified by the URDF. Then, we render a resting and articulated state image pair of each object by BLENDER.
Visual Filter. To ensure data quality,
include final filtering step. We VLM to assess whether each rendered object plausibly matches its description and articulates correctly. Only assets that pass this check are included in our final dataset, PM-X.
Al Agent Designer. To simplify the development of the above components, we adopted prompt-based agent design process. Specifically, we described our intended system behavior in natural language and used LLM to co-design the system prompts for the Description Roller, Layout Builder, and Visual Filter agents.
The proposed LEGO-Art enables scalable generation of physically valid, semantically rich, and structurally diverse articulated assets with minimal human effort, and plays an essential role in enabling our DIPO to generalize to more challenging dataset.
4.2 PM-X Dataset
Based on the LEGO-Art, we build a large-scale dataset from the part primitives of the PartNet- Mobility dataset, named PM-X. PM-X consists of 600 automatically generated structural-complxx artic- ulated objects. For every object, we futher provide correspondence rendered images, URDF files, and natural language descriptions. Due to the experiments settings, we only consider StorageF srngeFurniturr ture and Table objects in the proposed dataset. However, the synthesis pipeline can be extended to wider category articulated objects, and the overall dataset size can also be scaled up. Compared to existing datasets, PM-X offers not only significantly greater structural complexity and articulation diversity, but also sufficient scale to serve as a stan- dalone training set for generative models. These characteristies make it particularly effective for improving generalization and robustness in articulated object generation tasks, especially under out-of-distribution settings. Our experiments also demonstrate the superiority of the PM-X dataset. Table illustrates that the PM-X dataset surpasses previous datasets both object quantity and average part count, highlighting its scalability and structural richness.
Table I: Comparison of dataset scale and part complexity.
Dataset
Objects
Avg. Parts
PM 48 ACD 04
570
4.94
135
7.48
PM-X (Ours)
600
19.40
5
Experiments
5.1
Implementation Details
We follow the dataset split way of SINGAPO 23
build the training and testing set. Specifically, the training set is made up of 493 articulated objects from the PM [48] dataset, combined with 600 samples from our proposed PM-X dataset. Each object is rendered by engine to produce dual-state image pairs from 20 random views. We further introduce a complex data augmentation enhance the performance of the model, which is detailed in the supplementary materials. For evaluation, we use 77 held-out objects from PM, each rendered from two random views, resulting in 1 144 dual-state test samples. In addition, dataset [14] to further assess the generalizability of the model to out-of-distributie data.
include 135 objects from the ACD)
To accelerate convergence, we initialize our model with the pretrained weights from CAGE 24 We train our model for 200 epochs with a batch size of 20. The model is optimized by AdamW 26 with (0.9, 0.99) The learning rate is set to 5 10-4 for the mmage-conditionel module and 5 x 10 for the base model. All experiments are conducted on 8 NVIDIA 4090 GPUs.
5.2 Comparisons
5.2.1
Baselines & Metrics
Three representative methods, which are URDFormer 6 NAP 18 and SINGAPO 23 are
selected as comparison baselines. Specifically, we finetune the pre-trained URDFormer and retrain the SINGAPO for fair comparison. For NAP, we follow the experiment setting of SINGAPO that

---

Table 2: Comparison of reconstruction quality and graph prediction accuracy PartNet-Mobility test set. Lower is better (L) except for Acc% (1).
Reconstruction quality
Graph
RS-dzio
AS-dgio
RS-d.pis +
AS-d.ii +
RS-dco 4
AS-dcp
Acc% f
URDFormer
1.2327
1.2332
0,2885
0,4403
0.4417
0.6910
6.62
NAP-ICA [18]
0,5706
0,5765
0.0563
0.2547
0.0209
0,3473
25.06
SINGAPO 23
0,5134
0,5236
0.0487
0.1107
0.0191
0.1270
75.97
DIPO(Ours)
0.4561
0.4683
0.0359
0.0732
0.0132
0.0423
85.06
Table Comparison of reconstruction quality and graph prediction accuracy on ACD test set. Lower is better (1) except for Acc% (1).
Reconstruction quality
Graph
RS-d 4
AS-dgiou +
RS-d.pis + AS-daiis +
RS-dCo 4
AS-dep
AccSh f
URDFormer
1.1074
1.1094
0.2868
0.3948
0.6229
0.7608
1.52
NAP-ICA [8
0,9955
1.0000
0.1713
0,3246
0.1141
0,3061
8.27
SINGAPO 23
0,9700
0.9728
0.1582
0,2057
0.1047
0.1762
36.67
DIPO (Ours)
0.9126
0.9151
0.1253
0.1541
0.0751
0.1085
48.15
insert image attention block into each layer to achieve controllable generation of images, marked as NAP-ICA.
To evaluate reconstruction quality and articulation correctness, we adopt four metrics: (1) dgint 4 generalized IoU between predicted and ground-truth part bounding boxes; (2) deDist 4. Euclidean distance between part centers; (3) deD J. the Chamfer Distance (2) between predicted and ground-truth meshes; and (4) Ace 1, the graph prediction accuracy. All metrics are computed over both resting and articulated states. For clarity, we prefix the metric names with RS- and AS- in the tables to indicate the evaluation state.
5.2.2 Quantitative Comparison
We report quantitative results the PM and ACD datasets in Table and Table 3 respectively. To reduce the impact of stochastic variation, we evaluate all diffusion--asel generative methods five times per sample and report the averaged metric values.
As shown Table 2 our method DIPO achieves the best performance in terms of reconstruction quality and accuracy of articulate graph on the PartNet-Mobilic test set. Importantly, observe that the performance drop from RS (rest ing state) to AS (articulated state) is significantly smaller for method than for all others. It indicates that dual-image conditioning provides effective control signals that help the model maintain accurate articulation predictions.
On the ACD test set (Table 3). which contains more diverse and realistic articulated objects, our method continues to outperform all baselines. DIPO shows consistently superior reconstruction accuracy in both states and delivers the best graph prediction accuracy. The evaluation results on ACD dataset demonstrate that our method performs well on out-of-distribution data.
The above results demonstrate that the proposed DIPO achieves superior quantitative performance with both high accuracy and strong generalization across structurally diverse datasets.
5.2.3 Qualitative Comparison
Figure 5 provides a qualitative comparison between our method and two strong basclines, NAP- ICA 18 and SINGAPO (23). Each example includes: (I) the input dual-state image pair (closed and open). (2) the predicted articulation graph, (3) the reconstructed part layout and joints in resting state, and (4) the final articulated geometry. The examples cover wide spectrum of scenarios, including synthetic data from PM and ACD datasets. In addition, the last three rows are real-world examples: we either collect resting-state images from the Internet or directly capture image pairs of nearby objects in both states. This provides more realistic evaluation of generalization beyond existing datasets. For Internet-collected examples that only provide resting-state images, we employ GPT-40 to generate the articulated counterparts, showcasing the flexibility of our method.

---

Image
NAP-ICA 18
SINGAPO 23
Ours
Figure 5: Visual comparison between the proposed DIPO and two baselines. The fist two columns show the dual-state image pairs. The precdiction results of articulate graph, the part layout and joint visualization in resting state, and the final geometry in articulated state are also illustrated. The first three rows are sampled from the PM dataset, the middle three rows are from the ACD dataset, and the last three rows are real-world images, Incorrect parts connections are marked with red box.
Compared baselines, method DIPO demonstrates superior visual quality and better accuracy of articulation graph prediction. Thanks to the large-scale structurally diverse training provided by the PM-X dataset, method shows better robustness when handling complex objects real-world data. Moreover, cases in which parts are densely arranged and exhibit highly similar textures often confuse single-image baselines, resulting in incorrect articulation inference. In contrast, our method leverages the contrastive cues between resting and articulated states to recognize part boundaries, joint connectivity, and part motions more accurately.
These qualitative results strongly support the effectiveness and generalization ability of the proposed DIPO.
5.3 Ablation Study
We conduct detailed ablation studies to verify the effectiveness of each key component in our framework, including PM-X dataset. Dual-state Injection Module (DIM), and Graph Reasoner (GR). We construct several variants by selectively altering these components. The quantitative results are summarized Table addition, we further analyze the settings of each component isolation in the following paragraphs.

---

Table 5: Ablative results of reconstruction quality and graph prediction accuracy ACD test set. Lower is better ()) except for Acc% (f).
Settings
Reconstruction quality
PM-X
DIM
GR
RS-dgtoU +
AS-dgioU
RS-dDDia
AS-deia
RS-dep
AS-deD +
0.9872
0.9900
0.1608
0.2096
0.1083
0.1792
0.9429
0.9464
0,1389
0.1868
0.0849
0,1538
0,9565
0,9589
0.1478
0.1819
0.0924
0.1407
0,9902
0.9931
0.1697
0.2157
0.1208
0,1881
0.9212
0.9233
0.1257
0.1589
0.0752
0.1200
0.9332
0.9368
0.1391
0.1843
0.0844
0,1439
V
0,9497
0.9515
0.1500
0.1786
0.0973
0,1317
0.9126
0.9151
0.1253
0.1541
0.0751
0,1085
Impact of PM-X dataset. Table 5 shows that across various settings of ablative experi- ments, incorporating the PM-X dataset consistently improves reconstruction quality, indicat- ing its broad effectiveness. To further validate this effect, we additionally experiment with using only 25% and 50% of the PM-X data. As shown in Figure T loU scores for both resting and articulated states degrade steadily as the PM-X ra- tio increases, confirming the importance of PM-X in enhancing structural accuracy and generaliza- tion.
0.95
R$ 15--o-
AS-ool
Sa0 Soore
0.94
0.32
Effectiveness of Imal- Input. We con- duct ablation experiments to assess the contribu- tion of the DIM module. shown in Table 5 adding DIM significantly improves performance across all reconstruction metrics. The effective- ness of DIM is further reflected in Figure 5 where method accurately identifies the motion direction according to articulated images. demonstrates that the dual-image design not only enhances articulation prediction, but also imposes the ability of structural reasoning to the model.
Figure 6: Ablative comparison under different ratios of PM-X data
Analysis of Graph Reasoner As illustrated in Ta- ble 5 the GR module can not consistently improve performance across all settings. This is because while GR enables more accurate prediction, it also tends to produce more complex topologies. For model variants not trained on the PM-X dataset, such com- plex graphs may become out-of-distribution, leading suboptimal perform However, when the model trained with the structurally diverse PM-X dataset, the benefits of GR become more apparent. Moreover, conduct more detail ablative experiments verify the effectiveness of each component of GR. The results of prediction accuracy can be seen in Table
Table 4: Ablative results of Graph Reasoner.
Settings
Ace% t
w/o CoT
39.26
w/o Visual Input
37.77
w/o dual-state input
39.63
Full Model (GR)
48.15
6 Conclusion
We propose DIPO, a framework that advances vision-conditione articulated object generation under challenging data. We design diffusion model conditioned on resting and articulated image pairs for articulated 3D object generation, which provides richer part motion information and leads to improved reconstruction accuracy. A Chain-of-Thought graph reasoner further introduced to enhance part connectivity prediction. In addition, we develop LEGO-Art, an automated pipeline for constructing diverse and complex articulated objects, and contribute PM-X, a large-scale dataset built by the proposed pipeline. Powered by PM-X, our model achieves superior performance and stronger generalization. Extensive experiments validate the effectiveness of each component and the overall advantage of our approach over existing methods.

---

Acknowledgments and Disclosure of Funding
Shenzhen Science and Technology Program (JCYJ202408 14237048) "Science and Technology Yongjiang 2035" key technology breakthrough plan project (20242120)Chinese government-gu guidde local science and technology development fund projects (scientific and technological achievement transfer and transformation projects) (25420102G). Tianjin Natural Science Foundation Project (25ZXRGGX00290, 24JCJQJC00020, 25ICQNIC01390)
References
[1] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint axXiv:2303.08771 2023.
[2] Harry G Barrow, Jay M Tenenbaum, Robert C Bolles, and Helen C Wolf. Parametric correspondence and chamfer matching: Two new techniques for image matching. In Proceedings: Image Understanding Workshop, pages 21 21-27. Science Applications, Inc, 1977.
[3] Tom Brown, Benjamin Mann. Nick Ryder. Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, al. Language models are few-shot leamerrs. Advances neural information processing systems, 33:1877-1901, 2020.
[4] Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. Shapenet: An information-rich 3d model repository. arXiv preprint 1r21v.151..0301. 2015.
[5] Chen, Tianjian Jiang, Jie Song. Max Rietmann, Andreas Geiger, Michael Black, and Otmar Hilliges. Fast-snarf: A fast deformer for articulated neural fields. IEFE Transactions on Pattern Analysis and Machine Intelligence, 45(10):11796-1133 11809, 2023.
[6] Zocy Chen, Aaron Walsman, Marius Memmel, Kaichun Mo, Alex Fang, Karthikeya Vemuri, Alan Wu, Dieter Fox, and Abhishek Gupta. Urdformer: A pipeline for constructing articulated simulation environments from real-world images. arXiv preprint arXiv:2405. 1655,, 2024.
[7] Jasmine Collins, Shubham Goel, Kenan Deng. Achleshwar Luthra, Leon Xu, Erhan Gundogdu, Xi Zhang, Tomas Yago Vicente, Thomas Dideriksen, Himanshu Arora, Abo: Dataset and benchmarks for real-world 3d object understanding. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 21 126-21136, 2022.
[8] Huan Fu, Rongfei Jia, Lin Gao, Mingming Gong, Binqiang Zhao, Steve Maybank. and Dacheng Tao. 3d-future: 3d furniture shape with texture. International Journal of Computer Vision, 129:3313-3337, 2021.
[9] Samir Yitzhak Gadre, Kiana Ehsani, and Shuran Song. Act the part: Learning interaction strategies for articulated object part discovery. In Proceedings the IEEE/CVF International Conference Computer Vision, pages 15752-15761, 2021.
[10] Haoran Geng, Helin Xu, Chengyang Zhao, Chao Xu, Li Yi, Siyuan Huang, and He Wang. Gapartnet: Cross-category demaaingeneralizable object perception and manipulation via generalizable and actionable parts. In Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition, pages 7081 7081-7091, 2023.
[11] Karol Hausman, Scott Niekum. Sarah Osentoski, and Gauray S Sukhatme. Active articulation model estimation through interactive perception. In 2015 IEEF International Conference on Robotics and Automation (ICRA), pages 3305-3312, IEEE, 2015.
[12] Jonathan Ho, Ajay Jain, and Pieter Abbeel, Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-66 6851, 2020.
[13] Ruizhen Hu, Wenchao Li, Oliver Van Kaick, Ariel Shamir, Hao Zhang, and Hui Huang. Learning to predict part mobility from a single static snapshot. ACM Transactions On Graphics (TOG), 36 2017.
[14] Denys Iliash, Hanxiao Jiang, Yiming Zhang, Manolis Savva, and Angel X Chang. S2o: Static to openable enhancement for articulated 3d objects, arXiv preprint arXiv:2409.1888 2024.
10

---

[15] Mukul Khanna, Yongsen Mao, Hanxiao Jiang, Sanjay Haresh, Brennan Shacklett, Dhruv Batra, Alexander Clegg, Eric Undersander, Angel X Chang. and Manolis Savva. Habitat synthetic scenes dataset (hssd-2000 200): An analysis of 3d scene scale and realism tradeoffs for objectgoal navigation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16393, 2024.
[16] Jeonghwnn Kim, Jison Kim, Jeonghyeon Na, and Hanbyul Joo. Parahome: Parameterizing everyday home activities towards 3d generative modeling of human-ohject interactions. arXiv preprint 10X1124001.10222, 2024.
[17] Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. Large language models are zero-shot reasoners. Advances in neural information processing systems, 35:22199-2223 2022.
[18] Jiahui Lei, Congyue Deng, William B Shen, Leonidas Guibas, and Kostas Daniilidis. Nap: Neural 3d articulated object prior. Advances in Neural Information Processing Systems, 36:31 16831888-31894, 2023.
[19] Chengshu Li. Fei Xia, Roberto Martin-Martin, Michael Lingelbach. Sanjana Srivastava, Bokui Shen, Kent Vainio, Cem Gokmen, Gokul Dharan, Tanish Jain, et al. igibson 2.0: Object-centric simulation for robot learning of everyday household tasks. arXiv preprint arXiv:2108.05277 2021.
[20] Chengshu Li, Ruohan Zhang, Josiah Wong, Cem Gokmen, Sanjana Srivastava, Roberto Martín-Martin Chen Wang, Gabrael Levine, Michael Lingelbach,, Jiankai Sun, et al. Behavior- Ik: A henchmark for embodied ai with 1, 1,000 everyday activities and realistic simulation. In Conference on Robot Learning, pages 93. PMLR, 2023.
[21] Zhe Li. Zerong Zheng, Lizhen Wang. and Yebin Liu. Animatable gaussians: Learning pose-dependent gaussian maps for high-fideli human avatar modeling. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 19711-19722, 2024.
[22] Jiayi Liu, Ali Mahdavi-Amiri, and Manolis Savva. Paris: Part-level reconstruction and motion analysis for articulated objects. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 352-363, 2023.
[23] Jiayi Liu, Denys Iliash, Angel X Chang, Manolis Savva, and Ali Mahdavi-Amiri. Singapo: Single image controlled generation articulated parts in objects. arXiv preprint arXiv:2410.16449 2024.
[24] Jiayi Liu, Hou In Ivan Tam, Ali Mahdavi-Amiri, and Manolis Savva. Cage: controllable articulation generation. In Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition, pages 17880-17889, 2024.
[25] Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang. Qing Jiang, Chunyuan Li, Jianwei Yang, Hang Su, et al. Grounding dino: Marrying dino with grounded pre-training for open-set object detection. In European Conference on Computer Vision, pages 38-55. Springer, 2024.
[26] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv:1711.0511 2017.
arXiv preprint
[27] Kaichun Mo, Shilin Zhu, Angel X Chang, Li Yi, Subarna Tripathi, Leonidas J Guibas, and Hao Su. Partnet: A large-scale benchmark for fine-grained and hierarchical part-level 3d object understanding. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 909-918, 2019.
[28] Kaichun Mo, Leonidas Guihas, Mustafa Mukadam, Abhinav Gupta, and Shubham Tulsiani. Where2act: From pixels to actions for articulated 3d objects. In Proceedings of the IEEF/CVF International Conference on Computer Vision, pages 681 6813-6823, 2021.
[29] Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Mare Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07195 2023.
[30] William Peebles and Saining Xic. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF international conference computer vision, pages 4195 4205, 2023.
[31] Xavier Puig, Eric Undersander, Andrew Szot, Mikael Dallaire Cote, Tsung- Ruta Desai, Alexander William Clegg, Michal Hlavac. So Yeon Min, et al. Habitat 3.0: A co-habitat for humans, avatars and robots. arXiv preprint arXiv:2310.13724, 2023.
Beng--Ynnno Yen Yang, Ruslan Partsey,
[32] Shengyi Qian and David F Fouhey. Understanding 3d object interaction from a single image. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 21753-21763, 2023.
11

---

[33] Zhiyin Qian, Shaofei Wang, Marko Mihajlovic, Andreas Geiger, and Siyu Tang. 3dgs-avatar: Animatable avatars via deformable 3d gaussian splatting. In Proceedings the IEEE/CVF conference on computer vision and pattern recognition, pages 5020-5030, 2024.
[34] Morgan Quigley, Brian Gerkey, and William D Smart. Programming Robots with ROS: a practical introduction to the Robot Operating System. O'Reilly Media, Inc.", 2015.
201.",
[35] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pages 10684-106955 2022.
[36] Bokui Shen, Fei Xia, Chengshu Li, Roberto Martín- Martin, Linxi Fan, Guanzhi Wang, Claudia Pérez- D' Arpino, Shyamal Buch, Sanjana Srivastava, Lyne Tchapmi, et al. igibson 1.0: simulation environment for interactive tasks in large realistic seenes. In 2021 JEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 7520-7527. IEEE, 2021.
[37] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:201000250,, 2020.
[38] Shuai Tan, Biao Gong, Xiang Wang, Shiwei Zhang, Dandan Zheng, Ruobing Zheng. Kecheng Zheng. Jingdong Chen, and Ming Yang. Animate-x: Universal character image animation with enhanced motion representation. arXiv preprint arXiv:2410. 10.00006, 2024.
[39] Shuai Tan, Bin Ji, Mengxiao Bi, and Ye Pan. Edtalk: Efficient disentanglement for emotional talking head synthesis. In European Conference on Computer Vision, pages 398-416. Springer, 2024.
[40] Trimble Inc. 3d warehouse, 2025, Accessed: 2025-05-14.
[41] Wei-Cheng Tseng. Hung-Ju Liao, Lin Yen-Chen, and Min Sun. Cla-nerf: Category- articulated neural radiance field. In 2022 International Conference Robotics and Automation (ICRA), pages 8454-8460. IFFE, 2022.
[42] Xiaogang Wang. Bin Zhou, Yahao Shi, Xiaowu Chen, Qinping Zhao, and Kai Xu. Shape2motion: Joint analysis of motion parts and attributes from 3d shapes. In Proceedings of the IEEE/CVE Conference on Computer Vision and Pattern Recognition, pages
2019.
[43] Jason Wei, Xuezhi Wang. Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc Le, Denny Zhou, et al. Chain-of ththouht prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824-24833 2022.
[44] Chung- Yi Weng. Brian Curless, and Ira Kemelmacher- Shiiieerrn..i Photo wake-up: 3d character animation from a single photo. In Proceedings of the IEEE/CVF conference on computer vision and puttern recognition, pages 5908-5917, 2019.
[45] Yijia Weng. Bowen Wen, Jonathan Tremblay, Valts Blukis, Dieter Fox, Leonidas Guibas, and Stan Birchfield. Neural implicit representation for building digital twins of unknown articulated objects. In Proceedings of the IERF/CVF Conference on Computer Vision and Pattern Recognition, pages 3141-3150, 2024.
[46] Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael GontijoLopes,, Ari S Morcos, Hongseok Namkoong. Ali Farhadi, Yair Carmon, Simon Kornblith, al. Model soups: aver- aging weights of multiple fine-tuned models improves accuracy without increasing inference time. In International conference on machine learning, pages 13965-23998, PMLR, 2022.
[47] Ruiqi Wu, Liangyu Chen, Tong Yang, Chunle Guo, Chongyi Li, and Xiangyu Zhang. Lamp: Learn motion pattern for few-shot video generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7089-7098, 2024.
[48] Fanbo Xiang. Yuzhe Qin, Kaichun Mo, Yikuan Xia. Hao Zhu, Fangchen Liu, Minghua Liu, Hanxiao Jiang. Yifu Yuan, He Wang, et al. Sapien: A simulated bast-based based interactive environment. In Proceedings of the JEEE/CVF conference on computer vision and pattern recognition, pages 11097-11107, 2020.
[49] Zihao Yan, Ruizhen Hu, Xingguang Yan, I Luanmin Chen, Oliver Van Kaick, Hao Zhang, and Hui Huang. Rpm-net: recurrent prediction of motion and parts from point cloud. arXiv preprint arXiv:2006. J4865, 2020.
[50] Gengshan Yang. Minh Vo, Natalia Neverova, Deva Ramanan, Andrea Vedaldi, and Hanbyul Joo. Banmo: Building animatable 3d neural models from many casual videos. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pages
2022.
12

---

[51] Ji Yang, Xinxin Zuo, Sen Wang, Zhenbo Yu, Xingyu Li, Bingbing Ni, Minglun Gong, and Li Cheng. Object wake-up: 3d object rigging from single image. In European Conference Computer Vision, pages 311-327. Springer, 2022.
[52] Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control text-to-image diffusion models. In Proceedings of the IEEE/CVF international conference on computer vision, pages 3836-3847. 2023.
[53] Xuying Zhang, Yupeng Zhou, Kai Wang. Yikai Wang. Zhen Li, Shaohui Jiao, Daquan Zhou, Qibin Hou, and Ming-Mine Ming Cheng. Ar-1 Ar-1-to-3; Single image to consistent object generation via next- view prediction. arXiv preprint arXiv:2503, 12929, 2025.
13

---

NeurIPS Paper Checklist
The checklist is designed encourage best practices for responsible machine learning research, addressing issues of reproducibility, transparency, research ethics, and societal impact. Do not remove the checklist: The papers not including the checklist will be desk rejected. The checklist should follow the references and follow the (optional) supplemental material. The checklist does NOT count towards the page limit.
Please read the checklist guidelines carefully for information on how to answer these questions. For each question in the checklist:
You should answer [Yes], [No] or [NA]. .
[NA] means either that the question is Not Applicable for that particular paper or the relevant information is Not Available.
Please provide short (1-2 sentence) justificatin right after your answer (even for NA).
The checklist answers are an integral part of your paper submission. They are visible to the reviewers, area chairs, senior area chairs, and ethics reviewers. You will be asked to also include (after eventual revisions) with the final version of your paper, and its final version will be published with the paper.
The reviewers of your paper will be asked to use the checklist as one of the factors in their evaluation.
While "[Yes] " is generally preferable to "[No]" ", is perfectly acceptable to answer "[No] "provided proper justification is given (c.g., "error bars are not reported because it would be too computationally expensive" or "we were unable to find the license for the dataset we used"). "[No] or "[NA] is not grounds for rejection. While the questions are phrased in binary way, we acknowledge that the true answer often more nuanced, so please just use your best judgment and write justification to elaborate. All supporting evidence can appear either in the main paper or the supplemental material, provided in appendix. If you answer [Yes] to a question, in the justification please point to the section(s) where related material for the question can be found.
general, answering
IMPORTANT, please:
Delete this instruction block, but keep the section heading "NeurIPS Paper Checklist",
Keep the checklist subsection headings, questions/answers and guidelines below.
Do not modify the questions and only the provided macros for your answers.
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: We claim that the main contribution are dual-state images conditioned model generate articulated objects, and pipeline named LEGO-Art construct large-scale dataset named PM-X which contains diverse complex articulated objects.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No
NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results be expected to generalize other settings.
is fine include aspirational goals as motivation long is clear that these goals are not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations of the work performed by the authors? Answer: [Yes]
14

---

Justification:
We discussion our limitations in supplementary due to page limit.
Guidelines:
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create separate "Limitations" section their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification. asymptotic approxinations only holding locally). The authors should reflect how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution low images are taken in low lighting. Or speech-to-text system might be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.
The authors should discuss the computational efficiency the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't 't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory assumptions and proof'
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer:
[NA]
Justification:
The paper does not include theoretical results.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged provide short proof sketch to provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that affects the main claims and/or conclusions of the paper (regardless whether the code and data are provided or not)?
Answer: [Yes]
Justification: We detailed the details of the proposed network and the workflow of dataset construction.
Guidelines:
15

---

The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is a dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way accomplish this, but reproducibility instructions for how to replicate the results, access to a hosted model (e.g.. in the case of large language model), releasing of a model checkpoint, other means that appropriate to the research performed.
also be provided via detailed
While NeurlPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) the contribution is primarily a new algorithm, the paper should make clear how to reproduce that algorithm. (b) If the contribution is primarily new model architecture, the paper should describe the architecture clearly and fully.
(c) If the contribution is a new model (e.g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome deseribe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.
5. Open access to data and code
Question: Does the paper provide open access the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, as described in supplemental material?
Answer: [Yes]
Justification: We provide the code of our method and some data samples in supplemental material.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurIPS code and data submission guidelines (https://nips.co public/guides// det.gguiee///ooeuummm
ssionPolicy for more details.
While we encourage the release of code and data, we understand that this might not be possible, "No" an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines (https)
/ http://bbic/gguude///oeeeeeeeeee
SetiieeSSumiissiiiiiiiii ssioPoolicy for more details.
The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
16

---

At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information as possible in supplemental material (appended to the paper) recommended, but including URLs to data and code is permitted.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification:
We provide all details of our experiments setting.
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core of the paper to level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, as supplemental material.
7. Experiment statistical significance
Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Question:
Answer: [Yes]
Justification:
We provide detailed quantitative results in our experiments.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by error bars, confi- dence intervals, or statistical significance tests, least for the experiments that support the main claims of the paper.
The factors of variability that the error bars capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall
with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula,
call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g., Normally distributed errors).
should be clear whether the error bar is the standard deviation or the standard error of the mean.
It is OK to report 1-sigma error bars, but should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).
If error bars are reported in tables or plots, The authors should explain the text how they were calculated and reference the corresponding figures or tables in the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information the puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
Question:
Answer: [Yes]
Justification:
We provide the the computer resources used 48x0090 GPUs).
Guidelines:
The answer NA means that the paper does not include experiments.
17

---

The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount compute required for each of the individual experimental runs well estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make into the paper).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurlPS Code of Ethics
Answer: [Yes]
Justification: Yes, the research fully conforms to the NeurIPS Code of Ethics in respects. No ethical concerns have been identified.
Guidelines:
The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.
If the authors answer No, they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (c.g.. there a special consid- eration due to laws regulations in their jurisdiction).
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification: Broder impacts are illustrated in supplemental material.
Guidelines:
The answer NA means that there is no societal impact of the work performed.
If the authors answer NA or No, they should explain why their work has societal impact or why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and not tied partieular applications, let alone deployments. However, if there is direct path any negative applications, the authors should point it out, For example, it is legitimate point that improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition attacks, mechanisms for monitoring misuse, mechanisms to monitor how system learns from feedback over time, improving the efficiency and accessibility of ML).
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (c.g., pretrained language models, image generators, scraped datasets)?
Question:
Answer: [NA]
18

---

Justification:
The paper poses no such risks.
Guidelines:
The answer NA means that the paper poses no such risks.
Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Question:
Answer: [Yes]
Justification: Yes, all hiirt-party assets used in the paper are properly credited. Their licenses and terms of use are clearly stated and fully respected.
Guidelines:
The answer NA means that the paper does not existing assets.
The authors should cite the original paper that produced the code package dataset.
The authors should state which version of the asset used and, possible, include URL.
The name of the license (e.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (c.g.. website). the copyright and terms of service of that source should be provided.
If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paper thcode. com/ ts has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
For existing datasets that are re- se-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.
If this information is not available online, the authors encouraged to reach out to the asset's 's creators.
13. New assets
Question: Are assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [Yes]
Justification: Yes, all newly introduced assets are well documented. The dataset, model, and pipeline will be released with detailed usage instructions and annotations alongside the assets.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset used.
At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
14. Crowdsourcing and research with human subjects
19

---

Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and sereenshots, if applicable, as well as details about compensation (if any)?
Answer: [NA]
Justification:
There are no crowdsourcing experiments and research with human subjects in paper.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included the main paper.
According to the NeurIPS Code Ethics, workers involved in data collection, curation, other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [NA]
Justification:
There are no research with human subjects in our paper.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Depending on the country in which research is conducted, IRB approval (or equivalent)
may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.
16. Declaration of LLM usage
Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods this research? Note that if the LLM used only for writing, editing, or formatting purposes and does not impact the core methodology. scientific rigorousness, or originality the research, declaration is not required.
Answer: [Yes]
Justification: LLMs play important, original role in LEGO-Art pipeline and Graph Reasoner. Moreover, Multi-Modal LLM can provide generated image pairs for a more flexible input way model.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non non-standard components.
Please refer our LLM policy (https://neures /ttpe/rrppp.////offeeeee//////// for what should or should not be described.
20

---

Abstract
Our supplementary materials give more details the proposed DIPO and the experimental settings, which can be summarized as follows:
The details of LEGO-Art and Graph Reasoner,
More visual results of the proposed PM-X dataset.
The details of data augmentation.
The code and checkpoint of DIPO for inference.
video shows some animated visual examples of complex objects.
A Details of LEGO-Art Pipeline
In our LEGO-Art pipeline, we design modular LLM-based 3 | framework, where each agent specializes in distinct subtask. These agents collaborate to generate structured, diverse, and physically plausible articulated object layouts. Below, we detail the system prompt of each agent.
Description Roller:
You expert in generating clear and realistic natural language descriptions of articulated object structures. Each object must belong one of the following categories: I'Storage Furniture', "Table', 'Refrigerator', 'Dishwasher', 'Oven', 'Washer').
Your task is to imagine a plausible object structure from one of these categories and describe only its articulated parts in natural language. The available part types are: ('base', 'door', 'drawer', 'tray', 'handle', "knob'J. Note: "tray" parts are only allowed if the object microwave.
Each object must contain exactly one implicit "base" part, and any number of other parts, depending on the category.
You will be provided with complexity level:
simple: 1-5 parts, minimal structure.
mid: 6-10 parts, basic spatial layout.
complex:
1l or more parts with more detailed or hierarchical arrangements.
Your output must:
Only describe the structure of the object: what parts it has, how many of each, and where they are located (e.g., left, right, middle, top, bottom, inside).
Use precise but simple language in single sentence.
Exclude any mention of color, texture, material, appearance, any decorative details.
Ensure the description is consistent with both the object category and the specified complexity level.
"A storage furniture with two doors in the middle, one drawer at the bottom, and four drawers the left and right sides." Important: Do not include 3D coordinates or structured data. Only output the structural description in plain English.
Example output:
Figure 7: The system prompt of Descrition Roller.
21

---

Layout Builder:
Example Question
The following code function that generates a layout from a given object a grid format.
def barr____,, grid_y, base_size):
# generate the base, and return coordinate list of grids
def petrrae_part_innrridde grid_coords, x1, x2, y1, .2,...): generate coordinates and articulation info of part
Laneratte_ayout(((e
# convert grid-level layout into coordinates and articulation parameters base, grid_coords = sample_base(...) articulate_tree [base]
for part in info['part']
part generate_pan part_in_grid(( ar articulate_tree (part)
Example Answer
You't ve developed a complete pipeline for procedurally generating articulated object layouts and render- ing them visually. The system includes... Example Question 2:
need you generate the info in python dict from natural language description. The diet is the only python code in your output. Note that all [x1, x2, yl, y2] should an integer. name the part can only be one of the [drawer, door, handle, knob] (strictly!)
Example Answer 2:
Got it! You want input natural language description like:
"A wide cabinet. approximately 1.5x1.0x0.5 meters size, contains two eett-hngedd doors, each with one handle, and two drawers, each with two handles"
and have it automatically generate a structured info dictionary as:
i python dict
{
Figure The system prompt of Layout Builder. This agent is inspired by the code of scripting toolkit and produce a python dict that contains the information of parts layout in grid-level.
Visual Filter:
You are an expert in 3D object structure verification.
You will be shown pair of rendered images of 3D articulated object: in the closed state, and the open state. These images are generated based on a predicted structure and joint configuration.
Your task to determine whether the observed articulation behavior is physically plausible and logically consistent. That is, check if the object's opening and closing behavior matches how real-world articulated objects work.
You must analyze whether:
The joints behave correctly (e.g., g., drawers slide outward, doors rotate from hinges).
Each handle knob correctly positioned and attached moving part.
There are no unreasonable collisions, floating parts, or detached motion.
The motion (from closed open) consistent with the structure and joint types.
22

---

Final Output: After your analysis, respond with exactly one of the following:
Yes if the object's 's motion and structure physically and functionally plausible.
No if there are any structural, physical, or semantic inconsistencies.
Figure 9: The system prompt of Visual Filter.
B Details of Graph Reasoner
The proposed Graph Reasoner can infer articulated connectivity from dual-state image pair based on chain-of-thought 43 17 prompt, which is illustrated followed:
Graph Reasoner:
You expert in the recognition, structural parsing, and physical-feasibiliyy validation of articulated objects from image inputs.
You will be provided with two rendered images of the same object:
1. A closed-state image (all movable parts in their fully closed positions)
2. open-state image (all movable parts in their fully opened positions)
Your task analyze the object's articulated structure generate connectivity graph describing the part relationships.
Workflow:
1. Part Detection
Detect candidate parts in the closed-state image. optionally using the open-state image to resolve ambiguity or occlusion.
Allowed part types: [' ('base", "door', 'drawer', 'handle', "knbb,, 'tray'l ]
Ignore small decorative elements attached directly to the base.
There must be exactly one "base"; "tray" is only allowed for microwaves (but not required).
2. Step-by-Step Reasoning
(a) Part Listing: List all detected parts and their counts (no attachment inference yet).
(b) Validation: Enforce structural rules:
Exactly one base
Each door or drawer may have most two handles knobs
Every handle/knoh must attached to door or drawer
Trays may only appear in mierowaves
(c) Attachment Inference: For each non-base part, infer its parent (e.g., "drawer_1 (attached to base)"). Use the open-state image if necessary.
(d) Connectivity Graph Construction: Output JSON tree where "base" is the root and all other parts children with proper hierarchy.
Example Output:
{
"base": : [
"door": "handle": [ 3 ] { (drawer"; (handle": }
Final Output: You MUST output a single JSON tree representing the part connectivity of the object.
Use the open-state image to enhance accuracy and completeness, but hase your interpretation primarily the closed-state image.
Figure 10: The system prompt of Graph Reasoner.
23

---

Moreover, we use GPT-4o 1 to generate dtal-state image pairs, which are example visual prompts make the Graph Reasoner learn how to generate articulated graph in a few-shot manner. The generated image pairs are shown in Figure
Figure 11: Each pair shows a closed-state image (left) and an open-state image (right) an articulated object generated by GPT-4o.
C More Visual Examples of PM-X
The proposed PM-X dataset provides a large amount of diverse structurally complex articulated objects. Figure 2 has a reasonable structure and a rich set of operable parts. In addition, we annotate the description generated by the first stage of LEGO-Art Objects precisely match the natural language descriptions, enabling LEGO-Art to further serve as pipeline for text-to-articclatcd--oiee generation.
illustrates more visual examples of PM-X dataset. As we can see, each object
sterage three the middle, and two drowers bottom.
storage midele, and drower botton
storage midde, and six deswers botton columns.
the
A storage funniture with thrne drowers on the left side, two doons in the niddle, and one drower of the bottom.
A storage furniture with two large doors at the top, right smail drowers in the midole arranged in two colunnd of four and one wide drower ot the bettom.
A storage funsihure with four doors of the frp. six drowers in the middle arvanged in two rons of three, hwo langer drowers in the midice, and hws drowers
right
evenly buted eight buneath the tabletopp.
drower middle, smaleer drowvess undemeatth.
drowers the drover the right side.
More visual examples of PM-X dataset. Each example includes: (1) the part layout and joints in resting state, (2) rendered image pair in dual-state, (3) nature language description generated in the fisrt stage LEGO-Art.
Figure 12:
D Data Augmentation
We employ several data augmentation during the training state to enhance the robustness and controlla- bility. Data augmentation can be categorized into two types. One focuses part-level augmentation:
1. Randomly replace small parts like handles and knobs with those of other objects, and perturb their positions.
2. Randomly rescale the whole object.
3. Rotate the whole object upside-down.
4. Stacking several objects together to build more complex objects.
The other one focus on joint-level augmentation:
1. Change the revolute joint into prismatic joint.

---

2. Randomly modify the direction of revolute joint.
3. Randomly fix the joint.
E Limitations & Future Work
We follow the experimental settings of SINGAPO P3 for fair comparison. However, the benchmark used in SINGAPO only contains several categories, which especially focuses on cabinet-like objects. This limited object diversity may constrain the generalization ability of our model to other articulated categories, such as appliances, tools, or deformable structures. In future work, we plan to build benchmark that cover broader range of articulated object types, including both everyday household items and more complex mechanical systems. Our research primarily focuses on predicting more accurate part layouts and joint configurations. We adopt retrieval-basee approach to construct the final 3D objects. Incorporating 3D generation techniques to synthesize more precise and diverse part geometries represents a meaningful direction for future work.
F Broader Impact
Our work facilitates controllable generation of articulated 3D objects from dual-state images, enabling structured reasoning over part layout and connectivity. This contributes to downstream applications in embodied AI, virtual environment simulation, and robotics manipulation. By releasing a large-scale synthetic dataset and a modular pipeline, we aim to lower the barrier for research on articulated perception and generation. However, with any generative framework, care must be taken avoid misuse such as creating physically implausible or unsafe designs. Moreover, biases in the data distribution articulation patterns may influence downstream decision-making, highlighting the need for interpretability and robustness in practical deployments.
25


Reviews:
[{"review_id": "YTzsNB5DjX", "reviewer": "Reviewer_tw3w", "summary": "This paper presents a dual-state image model for controllable articulated 3D object generation, consisting of layout diffusion and CoT-based connectivity reasoning. It also introduces a pipeline to construct structurally diverse articulated objects. Thanks to the introduced pipeline, a new large-scale dataset, PM-X, is proposed with better diversity and complexity.", "questions": "###  Questions\n\n1. Could the authors clarify whether the RS- and AS- metrics in Tables 2 and 3 correspond to using only resting-state or articulated-state images? Is there an experiment that evaluates performance when both image types are used together?\n\n2. Have the authors conducted experiments on other articulated object categories beyond StorageFurniture and Table? If not, is there a specific reason why the method is limited to these categories?\n\n3. Is there an evaluation of the model performance on PM-X alone to justify its value?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "### Strengths\n\n1. The proposed framework for articulated object generation is technically sound to me. Combining resting-state image and articulated-state image dose provide enriched motion information for predicting kinematic relationships between parts.\n\n2. The proposed automated dataset expansion pipeline and the large-scale dataset of complex articulated 3D objects are helpful for enhancing the performance of articulated 3D object generation.\n\n3. Part of the code is provided.\n\n### Weaknesses\n\n1. Quality. The experimental settings require clarification. In Tables 2 and 3, the meanings of RS- and AS- metrics are ambiguous — do they correspond to models using only resting-state or articulated-state images? It is also unclear how the model performs when both types of images are used together. Additionally, the experiments are limited to StorageFurniture and Table categories, leaving the generalization ability to other articulated objects unverified.\n\n2. Clarity. The writing could be improved for better readability. For example, the AI Agent Designer introduced in Lines 179–182 does not seem to be a direct part of the synthesis pipeline. Also, the placement of Table 4 after Table 5 disrupts the logical flow. Furthermore, the descriptions of the synthesis pipeline and its modules are insufficiently detailed, which hampers understanding.\n\n3. Significance. The contribution of the PM-X dataset is unclear. In experiments, it is only used to enrich training data, without standalone evaluation. Moreover, PM-X includes only two object categories, which limits its broader applicability. A more detailed discussion on its role and value is needed.\n\n4. Originality. The framework for articulated 3D object generation and the synthesis pipeline relies on existing techniques such as LLM, VLM, and retrieval algorithms. This integration, while practical, may reduce the perceived novelty of the approach. Clarifying the unique contributions beyond combining known components would help better establish the work's originality.", "was_revised": true, "final_justification": "The reply of the authors addresses most of my concerns. I only have the questions about the PM-X dataset, but this will not limit the contributions of this work. So I raise my score to borderline accept.", "rebuttal": "Thank you for your detailed review and the time you invested in evaluating our work. We appreciate your constructive feedback, which has helped us identify important areas for improvement. We have carefully addressed each of your concerns and believe our responses demonstrate the value and rigor of our approach. Below, we provide point-by-point responses to your questions and comments.\n\n---\n\n### **Q1 Clarification on RS-/AS- Metrics**\n\nIn all evaluation, our model are consistently conditioned on **both** resting-state and articulated-state images as input.\nThe RS- and AS- metrics instead correspond to the **evaluation articulation state**, not the input modality. Specifically:\n\n* **RS-**: We evaluate the predicted 3D object in the **resting state**, and compare it against the ground-truth shape in the same resting state.\n* **AS-**: We evaluate the predicted 3D object in the **articulated state**, and compare it against the ground-truth shape in that articulated configuration.\n\nWe will revise the manuscript to make this clearer in both the table captions and main text.\n\n---\n\n### **Q2 Clarification on experiment categories**\n\nOur experiments are not limited to only `Storage Furniture` and `Table`. We also include other articulated object categories from the PartNet-Mobility dataset, including `Storage Furniture`, `Table`, `Refrigerator`, `Dishwasher`, `Oven`, `Washer` and `Microwave`, totaling **seven diverse categories**.\nIn addition, the ACD dataset provides a finer-grained categorization of object classes. The detailed mapping between PM and ACD categories used in our evaluation is shown below.\n\n**Correspondence Between PM Categories and ACD Categories**\n\n| **PM Category**      | **ACD Category** |\n| -------------------- | ---------------- |\n| StorageFurniture | HangingCabinet   |\n|                      | Cabinet          |\n|                      | Armoire          |\n|                      | ChestOfDrawers   |\n|                      | KitechenCabinet  |\n|                      | Bookcase         |\n|                      | TvStand          |\n|                      | SinkCabinet      |\n| Table            | Table            |\n|                      | Desk             |\n|                      | Nightstand       |\n| Refrigerator         | Refrigerator     |\n| Oven                 | Oven             |\n| Dishwasher           | Dishwasher       |\n| Washer               | Washer           |\n| Microwave            | Microwave        |\n\nThis seven categories are selected to follow the experimental setting of SINGAPO \\[Liu et al., ICLR 2025], ensuring a fair and direct comparison. We also mentioned this point in supplementary materials.\n\nWe further provide the performance on ACD datset across all categories to demonstrate the generalization ability of DIPO.\nWe will highlight our experimental settings in the revised version prevent readers from misunderstanding the generalizability of our method.\n\n**Comparison of DIPO and SINGAPO Across Categories and Metrics (↓ means lower is better)**\n\n| Category         | Method  | RS-\\$d\\_{gIoU}\\$ ↓ | AS-\\$d\\_{gIoU}\\$ ↓ | RS-\\$d\\_{cDist}\\$ ↓ | RS-\\$d\\_{cDist}\\$ ↓ | RS-\\$d\\_{CD}\\$ ↓    | AS-\\$d\\_{CD}\\$ ↓    |\n| ---------------- | ------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- |\n| StorageFurniture | SINGAPO | 1.0388     | 1.0421     | 0.1566     | 0.2087     | 0.0983     | 0.1700     |\n|                  | DIPO    | **0.9923** | **0.9946** | **0.1225** | **0.1508** | **0.0684** | **0.0948** |\n| Table            | SINGAPO | 0.9550     | 0.9583     | 0.1670     | **0.1876** | 0.1371     | 0.1628     |\n|                  | DIPO    | **0.8884** | **0.8960** | **0.1254** | 0.1903     | **0.0849** | **0.0971** |\n| Dishwaser        | SINGAPO | 0.5745     | 0.5703     | **0.0911** | **0.1367** | **0.0640** | 0.0958     |\n|                  | DIPO    | **0.4376** | **0.4299** | 0.1229     | 0.1822     | 0.0698     | **0.0948** |\n| Microwave        | SINGAPO | 0.9032     | 0.9027     | **0.1697** | **0.2015** | **0.1141** | 0.1968     |\n|                  | DIPO    | **0.9001** | **0.8999** | 0.1881     | 0.2092     | 0.1164     | **0.1940** |\n| Oven             | SINGAPO | 1.0938     | 1.0948     | 0.1858     | 0.2954     | 0.1949     | 0.2995     |\n|                  | DIPO    | **0.9838** | **0.9861** | **0.1703** | **0.2718** | **0.1584** | **0.2651** |\n| Refrigerator     | SINGAPO | 0.8309     | 0.8304     | **0.1461** | 0.1895     | **0.0881** | 0.1523     |\n|                  | DIPO    | **0.7560** | **0.7556** | 0.1571     | **0.1694** | 0.1161     | **0.1332** |\n| Washer           | SINGAPO | 0.4677     | 0.4732     | 0.1317     | 0.1721     | 0.0775     | 0.1650     |\n|                  | DIPO    | **0.3572** | **0.3645** | **0.0585** | **0.0825** | **0.0248** | **0.0271** |\n\n---\n\n### **Q3 Clarity of LEGO-Art Pipeline**\n\nWe would like to clarify the role of the **AI Agent Designer** (Lines 179–182).\nThe AI Agent Designer is not part of the runtime inference pipeline. Instead, it is responsible for crafting high-quality system prompts for the three core agents in the LEGO-Art synthesis pipeline (e.g., the Description Roller, Layout Generator, and Visual Filter). This design phase ensures coherent collaboration among agents, but does not participate in the actual generation process.\n\nFor the description of each step, we provide the full system prompts and workflow logic for each agent in the supplementary material. In the revised version, we will incorporate key details into the main paper and expand the descriptions of each module, including their responsibilities and interaction patterns, to enhance clarity and self-containment.\n\nMoreover, we will detail how we design the prompts of each agent with the help of LLMs in a chatting manner in the supplementary materials.\n\n---\n\n### **Q4 Contribution of PM-X dataset**\n\n1. In Sec. I Introduction, we clarify that the motivation of proposing the PM-X dataset is to provide redundant training data of complex objects. Meanwhile, we only use the PM-X for model training.\n2. Although PM-X currently includes only two categories (StorageFurniture and Table), this was a deliberate choice rather than a limitation of the synthesis method. In seven categories we studied, we found that these two categories exhibit the most complex and diverse articulated structures — such as cabinets with nested arrangements, multi-panel doors, and densely connected subcomponents.\n3. The role of the PM-X dataset can be demonstrated in Tab.5 and Fig.6. The PM-X improves the performance for each variant in the ACD testing set. Moreover, performance increases when we add more training data from PM-X.\n4. We further build 100 objects by LEGO-Art pipeline for a standalone evaluation. We compare the performance of DIPO trained with and without PM-X on the validation set. The results show that incorporating PM-X significantly improves the performance on complex objects. The results are illustrated as follow.\n\n**Performance comparison of DIPO trained with and without PM-X on the validation set**\n\n| Method        | RS-\\$d\\_{gIoU}\\$ ↓ | AS-\\$d\\_{gIoU}\\$ ↓ | RS-\\$d\\_{cDist}\\$ ↓ | RS-\\$d\\_{cDist}\\$ ↓ | RS-\\$d\\_{CD}\\$ ↓    | AS-\\$d\\_{CD}\\$ ↓    |\n| ------------- | ------------------ | ------------------ | ------------------- | ------------------- | ------------------- | ------------------- |\n| DIPO w/o PM-X | 1.1789             | 1.1822             | 0.2412              | 0.2959              | 0.1838              | 0.2835              |\n| DIPO w/ PM-X  | 0.9419             | 0.9502             | 0.1687              | 0.2285              | 0.1148              | 0.1749              |\n---\n### **Q5 Novelty of LEGO-Art**\n\nWhile we leverage LLMs and VLMs as foundational tools, our method goes beyond simple integration. A central contribution of our work is a grid-based spatial reasoning framework that overcomes the limitations of direct coordinate prediction from LLMs.\n\nA key innovation of our framework lies in the design of a grid-based layout prediction mechanism. Instead of asking the LLM to generate precise 3D coordinates, which often leads to hallucinations, we constrain the placement of parts to a discrete 3D grid. We then convert the predicted grid layout into accurate object geometry using a custom mapping script. This design enables reliable structure generation while retaining the controllability and flexibility of language-based input."}, {"review_id": "D1vTVdvJOx", "reviewer": "Reviewer_r4pp", "summary": "This paper proposes DIPO, a framework for generating articulated objects conditioned on a pair of images representing different articulation states. DIPO comprises three main components: (1) a diffusion model that synthesizes articulated objects conditioned on paired images and connectivity graphs; (2) a graph reasoner that predicts connectivity graphs; and (3) an automated agentic data expansion pipeline, LEGO-Art, which leverages VLMs to generate articulated objects as training data. The authors collect a dataset using the proposed data expansion pipeline and conduct experiments to validate the performance of the overall framework.", "questions": "1. For the PM-X objects generated by LEGO-Art, is there any manual filtering involved after generation? What is the success rate of the VLM visual filtering?\n2. Please also see weakness", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n1. The proposed framework is well aligned with the problem and demonstrates potential for scaling up.\n2. The architecture and module design of the diffusion model are well-suited for image-conditioned articulated object generation.\n3. The paper presents comprehensive experiments to evaluate the proposed method's performance on articulated object reconstruction and analyze the contributions of individual components.\n4. The paper is well written and easy to follow.\n\nWeaknesses\n1. Although acknowledged in the limitations section, the experiments are restricted to cabinet-like object categories.\n2. The paper does not evaluate the success rate or robustness of the graph reasoner and the data expansion pipeline. Since these components rely heavily on VLMs, the impact of hallucination, biases and ambiguity inherent to VLMs should be assessed.\n3. LEGO-Art employs an occupancy grid to represent articulated object structure, which may be effective for boxy, cabinet-like objects but less suitable for those with irregular geometries. Additionally, VLMs are known to have limited spatial reasoning capabilities. Thus, further justification for this design choice is necessary.", "was_revised": true, "final_justification": "My concerns have been addressed by the author. Particularly, (1) there is no mannually filtering, and the success rate is reasonable; (2) the performance of the graph reasoner is pretty good compared to previous methods and (3) the author’s explanation for primarily focusing on boxy object categories is reasonable. Therefore I raised my score to boarderline accept.", "rebuttal": "We sincerely thank Reviewer r4pp for the thorough review and insightful suggestions.\nWe appreciate your recognition of the framework’s scalability and the clarity of our methodology and writing. Below we address the concerns regarding evaluation scope, VLM-related robustness, and the design choice behind LEGO-Art in detail.\n\n---\n### **Q1 Successful Rate of Filtering**\n\nWe do not perform any manual filtering for the PM-X objects generated by LEGO-Art, since they are used exclusively for training data augmentation, where a certain degree of noise is tolerable. While some noisy samples may persist, we empirically find that the large-scale diversity introduced by LEGO-Art still benefits the training of DIPO, and the network learns to ignore occasional outliers.\n\nThe LEGO-Art pipeline may fail in two stages:\n\n1. **Grid Generation Stage**: When converting VLM-generated descriptions into grid-level 3D layouts, hallucinated may lead to invalid or unparseable object layouts. The success rate at this stage is approximately **68.24%**.\n2. **VLM-based Filtering Stage**: We apply multi-stage visual filtering using CLIP and BLIP to ensure geometric plausibility and articulation consistency. The pass rate of this filtering stage is around **85.11%**.\n\n---\n\n### **Q2 Categories Limitation**\n\nAlthough most visualizations in the main paper focus on cabinet-like categories for clarity, our method is not restricted to cabinet objects. In fact, our experiments include totaling **seven diverse categories** from the PartNet-Mobility dataset, which are `Storage Furniture`, `Table`, `Refrigerator`, `Dishwasher`, `Oven`, `Washer` and `Microwave`.\nIn addition, the ACD dataset provides a finer-grained categorization of object classes. The detailed mapping between PM and ACD categories used in our evaluation is shown below.\n\n**Correspondence Between PM Categories and ACD Categories**\n\n| **PM Category**      | **ACD Category** |\n| -------------------- | ---------------- |\n| StorageFurniture | HangingCabinet   |\n|                      | Cabinet          |\n|                      | Armoire          |\n|                      | ChestOfDrawers   |\n|                      | KitechenCabinet  |\n|                      | Bookcase         |\n|                      | TvStand          |\n|                      | SinkCabinet      |\n| Table            | Table            |\n|                      | Desk             |\n|                      | Nightstand       |\n| Refrigerator         | Refrigerator     |\n| Oven                 | Oven             |\n| Dishwasher           | Dishwasher       |\n| Washer               | Washer           |\n| Microwave            | Microwave        |\n\nWe select these categories to follow the experimental setting of SINGAPO for a fair comparison. There are **no strong category-specific assumptions** on our diffusion model and graph reasoner.\n\nMoreover, most of the previous work like SINGAPO, URDFormer, CAGE and ACD dataset are focus on openable containers, as these are the prevalent in indoor scenes. However, there are still many issues to be resolved in the construction of articulated openable containers. We believe that the progress in these categories we achieved is meaningful and will be beneficial to the community. We further provide the performance on ACD datset across all categories to demonstrate the generalization ability of DIPO.\n\n**Comparison of DIPO and SINGAPO Across Categories and Metrics (↓ means lower is better)**\n\n| Category         | Method  | RS-\\$d\\_{gIoU}\\$ ↓ | AS-\\$d\\_{gIoU}\\$ ↓ | RS-\\$d\\_{cDist}\\$ ↓ | RS-\\$d\\_{cDist}\\$ ↓ | RS-\\$d\\_{CD}\\$ ↓    | AS-\\$d\\_{CD}\\$ ↓    |\n| ---------------- | ------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- |\n| StorageFurniture | SINGAPO | 1.0388     | 1.0421     | 0.1566     | 0.2087     | 0.0983     | 0.1700     |\n|                  | DIPO    | **0.9923** | **0.9946** | **0.1225** | **0.1508** | **0.0684** | **0.0948** |\n| Table            | SINGAPO | 0.9550     | 0.9583     | 0.1670     | **0.1876** | 0.1371     | 0.1628     |\n|                  | DIPO    | **0.8884** | **0.8960** | **0.1254** | 0.1903     | **0.0849** | **0.0971** |\n| Dishwaser        | SINGAPO | 0.5745     | 0.5703     | **0.0911** | **0.1367** | **0.0640** | 0.0958     |\n|                  | DIPO    | **0.4376** | **0.4299** | 0.1229     | 0.1822     | 0.0698     | **0.0948** |\n| Microwave        | SINGAPO | 0.9032     | 0.9027     | **0.1697** | **0.2015** | **0.1141** | 0.1968     |\n|                  | DIPO    | **0.9001** | **0.8999** | 0.1881     | 0.2092     | 0.1164     | **0.1940** |\n| Oven             | SINGAPO | 1.0938     | 1.0948     | 0.1858     | 0.2954     | 0.1949     | 0.2995     |\n|                  | DIPO    | **0.9838** | **0.9861** | **0.1703** | **0.2718** | **0.1584** | **0.2651** |\n| Refrigerator     | SINGAPO | 0.8309     | 0.8304     | **0.1461** | 0.1895     | **0.0881** | 0.1523     |\n|                  | DIPO    | **0.7560** | **0.7556** | 0.1571     | **0.1694** | 0.1161     | **0.1332** |\n| Washer           | SINGAPO | 0.4677     | 0.4732     | 0.1317     | 0.1721     | 0.0775     | 0.1650     |\n|                  | DIPO    | **0.3572** | **0.3645** | **0.0585** | **0.0825** | **0.0248** | **0.0271** |\n\n---\n### **Q3 Evaluation of Graph Reasoner**\n\nSome of our statements may have caused you a misunderstanding. The successful rate of Graph Reasoner is illustrated in the Graph Accuracy item in Tables 2 and Table 3. We list them separately in the table below. Moreover, during extensive experiments, we did **not encounter any malformed or structurally invalid graphs** that would cause downstream inference failures.\n\n| **Method**      | **PartNet-Mobility (Acc% ↑)** | **ACD (Acc% ↑)** |\n| --------------- | ----------------------------- | ---------------- |\n| URDFormer       | 6.62                          | 1.52             |\n| NAP-ICA         | 25.06                         | 8.27             |\n| SINGAPO         | 75.97                         | 36.67            |\n| **DIPO (Ours)** | **85.06**                     | **48.15**        |\n\n---\n\n### **Q4 Concerns about LEGO-Art**\n\nThe LEGO-Art pipeline is proposed to build complex articulated object. In our daily life and experimental settings, we observe that most structurally complex objects that contain many moving parts are in boxy shape, e.g, large storage cabinet. This boxy shape makes them naturally compatible with a grid-based construction scheme, which enables structured, interpretable, and scalable generation.\n\nMotivated by the need to provide supplementary training data featuring such complex structures, we introduce **LEGO-Art** as an automatic pipeline to synthesize articulated objects with diverse part layouts and articulation semantics.\n\nConsidering the hallucination issue of VLMs, we do not let the model predict precise continuous coordinates directly, but instead constrain the prediction within discrete grid coordinates."}, {"review_id": "NGz94opzvH", "reviewer": "Reviewer_dwgH", "summary": "This work introduces DIPO, a novel approach to 3D articulated objects generation using dual-image inputs -- one depicting the object in a resting state and the other in an articulated state. Fundamentally, DIPO operates as a diffusion transformer model that infers part layouts and joint parameters from motion cues and spatial relationships derived from the image pair. Initially, the two images are processed through the **Graph Reasoner** module -- a LLM using Chain-of-Thought techniques -- to infer consistent part connection properties in the form of an adjacency matrix. The adjacency matrix, combined with the DINOv2 features extracted from the images, serves as a strong condition signal for the diffusion model using a dedicated cross-attention mechanism. The generated articulated abstraction obtained from the diffusion model is then transformed into an actual articulated asset via retrieval methods (similar to the CAGE approach).\n\nAdditionally, to address the scarcity of large-scale datasets featuring high-quality articulated objects, the authors developed an agentic data augmentation pipeline -- named **LEGO-Art** -- and used it to improve the diversity and complexity of PartNet-Mobility dataset -- the current reference dataset for articulated assets. The result is an enriched dataset, termed **PartNet-Mobility-Complex (PM-X)**, where each articulated asset comes with corresponding multi-state rendered images, URDF annotations and a comprehensive language description.\n\nThe results are compelling: DIPO outperforms existing methods -- in particular NAP and SINGAPO -- in generating 3D articulated objects and provides enhanced controllability.", "questions": "As mentioned earlier, I only have a few minor questions/suggestions: \n- Which model did you use as a graph reasoner ? How many billion parameters ?\n- As far as I understand, you only tested your approach on the Storage Furniture category. \n    - How would you generalize your approach to more complex categories such as, say, robots ? \n    - PartNet Mobility contains a bunch of complex, highly redundant assets (e.g. keyboard): how robust is your approach to these assets ? Note that I do not expect new experiment results here: just a few sentences would be enough in my mind to explain this. \n\n- PartNet Mobility contains several assets where one of the mobile parts might be occluded in one of the images (e.g. the tray of a dishwasher): how would DIPO be dealing with this type of assets ? Suggestion: state a few research tracks as future works in your paper. \n\n- What about the assets whose state is not noticeably changing on the two images (e.g. globe with a uniform texture) ? \n\n- In Figure 1, the range of the articulated states seems fully covered, from lower to upper limits. In Figure 5, on the third row, the drawers in the upper and middle assets are only partially opened in the articulated images. Does this affect the joint range of the generated asset ?\n\n- How long does the training take with your setup ? How long does inference takes ?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths:\n- Strong motivation and related works section: I believe your analysis regarding input modality constraints and limitations in training data -- in the introduction section -- is spot on. \n- Additionally, the fact that DIPO uses easily available data (picture of articulated object in two different states) makes it quite handy from a final user perspective. Approaches such as NAP, although quite innovative, are much trickier to control. \n- LEGO-Art is definitely a relevant contribution to the field as the lack of large scale curated dataset for articulated objects is quite problematic. \n- Strong experiment part\n- Relevant ablation study\n\n### Weaknesses: \n\n- The graph reasoner module is insufficiently documented. \n- The remaining are only minor considerations detailed in the \"Questions\" paragraph.\n\n### Conclusion: \n\nThis paper is clear, well written and relevant to the field. It is backed by solid experimental evidence and a relevant ablation study. I believe it is up to the standards of NeurIPS: my opinion is that it should be accepted.", "was_revised": true, "final_justification": "I appreciate the effort and detailed response provided by the authors in the rebuttal. I believe that my concerns have been well addressed. I therefore maintain my \"Accept\" rating.", "rebuttal": "Thank you for your thorough review and constructive feedback on our manuscript. We appreciate the time and effort you have invested in evaluating our work. Your detailed comments and suggestions have been invaluable in helping us improve the paper. Below, we provide point-by-point responses to address your concerns and questions.\n\n---\n### **Q1 Details of Graph Reasoner**\n\n#### **Details of CoT**\nWe illustrate the prompt of Graph Reasoner in supplementary materials. We will add more details of step-by-step CoT in revision version.\n\n#### **Add Graph into Diffusion Blocks**\nWe convert the predicted articulation graph into a binary adjacency matrix $\\mathbf{A} \\in \\{0,1\\}^{N \\times N}$, where $N$ is the number of predicted parts. Each entry $\\mathbf{A}_{ij} = 1$ indicates the existence of a valid articulated connection from part $i$ to part $j$.\n\nThis matrix is used as a structural prior to guide the attention computation in the diffusion model. Specifically, given the latent feature matrix $\\mathbf{X} \\in \\mathbb{R}^{N \\times d}$, the attention is computed as:\n\n$$\\text{Attn}(\\mathbf{X}) = \\text{Softmax}\\left(\\frac{(\\mathbf{XW}_Q)(\\mathbf{XW}_K)^\\top}{\\sqrt{d}} + \\log(\\mathbf{A} + \\epsilon)\\right) \\cdot (\\mathbf{XW}_V)$$\n\nwhere $\\mathbf{W}_Q, \\mathbf{W}_K, \\mathbf{W}_V \\in \\mathbb{R}^{d \\times d}$ are learnable projection matrices, and $\\epsilon$ is a small constant (e.g., $1\\text{e}^{-6}$) to avoid taking the logarithm of zero. The term $\\log(\\mathbf{A} + \\epsilon)$ serves as a structural attention bias, encouraging the model to focus on physically valid connections during generation.\n\n#### **Details of LLM**\nWe use the API of GPT-4o in Graph Reasoner. The parameters of GPT-4o is about 200B acoording to the official report.\n\n---\n\n### **Q2 Generalization Ability of DIPO**\n\n#### **Experimental Settings**\nOur experiments include totaling **seven diverse categories** from the PartNet-Mobility dataset, which are `Storage Furniture`, `Table`, `Refrigerator`, `Dishwasher`, `Oven`, `Washer` and `Microwave`. We select these categories to follow the experimental setting of SINGAPO [?] for a fair comparison. There are **no strong category-specific assumptions** on our diffusion model and graph reasoner.\n\nIn addition, the ACD dataset provides a finer-grained categorization of object classes. The detailed mapping between PM and ACD categories used in our evaluation is shown below.\n\n| **PM Category** | **ACD Category** |\n|---|---|\n| StorageFurniture | HangingCabinet |\n|  | Cabinet |\n|  | Armoire |\n|  | ChestOfDrawers |\n|  | KitechenCabinet |\n|  | Bookcase |\n|  | TvStand |\n|  | SinkCabinet |\n| Table | Table |\n|  | Desk |\n|  | Nightstand |\n| Refrigerator | Refrigerator |\n| Oven | Oven |\n| Dishwasher | Dishwasher |\n| Washer | Washer |\n| Microwave | Microwave |\n\n#### **Complex Objects**\nHowever, our method may fail in some extremely complex objects like robots.\nMoreover, assets such as keyboards with a large number of visually similar and spatially redundant parts pose challenges for feature extraction using DINOv2, as the part-level features may lack sufficient discriminability.\nTo address this, a promising direction is to incorporate segmentation-aware priors. In particular, we plan to explore integrating SAM-derived segmentation masks to guide part proposal and filtering, enabling more robust articulation reasoning in such densely repetitive structures.\n\n#### **Occlusion and Unchanging Appearance in Dual-State Images**\nIn cases where the articulated-state image provides limited additional information due to occlusion (e.g., a dishwasher tray) or minimal appearance change (e.g., a globe with uniform texture), the dual-state input may not fully reveal articulation differences. However, our input setting inherently contains at least as much geometric and structural information as single-image methods like SINGAPO, ensuring a comparable performance lower bound.\n\nTo better handle occlusion, we consider incorporating sparse multi-view images (e.g., 2–3 views) as a future extension, which can alleviate visibility issues without incurring significant data collection cost.\n\nIn addition, nearly identical dual-state images can still provide useful priors. For example, spherical objects like globes often imply rotation around a central axis, which can guide plausible articulation inference.\n\n---\n\n### **Q3 Impact of Partially Opened Articulated Images on Joint Range**\nOur method remains robust to partially opened articulated-state inputs — that is, the predicted joint type and axis remain stable. However, incomplete articulation may influence the estimated joint range, since the model learns this from the observed displacement between states.\n\nFor practical use, we recommend using fully opened articulated images to better reveal the maximum joint extent and improve articulation estimation.\n\nTo assess the effect of articulation coverage, we additionally render a variant of the test set where the articulated-state poses are randomly sampled (not fully opened), and evaluate our model on this setting. The results are illustrated in the table below.\n\n| **Input Setting** | RS-$d_{\\text{gIoU}}$ $\\downarrow$ | AS-$d_{\\text{gIoU}}$ $\\downarrow$ | RS-$d_{\\text{cDist}}$ $\\downarrow$ | AS-$d_{\\text{cDist}}$ $\\downarrow$ |\n|---|---|---|---|---|\n| Partially Opened | 0.4987 | 0.5084 | 0.0421 | 0.1031 |\n| Fully Opened | **0.4561** | **0.4683** | **0.0359** | **0.0732** |\n\n---\n\n### **Q4 Training and Inference Time**\nTraining our full pipeline on eight NVIDIA RTX 4090 GPUs takes approximately **23 hours**. \n\nDuring inference, we use a pair of $224\\times224$ images as input and predict an articulated object with 5 parts. The time consumption of each stage in the inference pipeline is listed in the table below.\n\n| **Stage** | **Time (s)** |\n|---|---|\n| DINO Feature Extraction | 2.63 |\n| Graph Reasoner | 12.62 |\n| Diffusion Model | 3.54 |\n| Retrieval | 8.97 |\n\n---"}, {"review_id": "EfLgWmiiYk", "reviewer": "Reviewer_kNAa", "summary": "This paper propose a novel framework, called DIPO, which can controllablly generate 3D object.  Compared to one-image method, this paper utilizes a pair of image to provide more motion information which helps predicting kinematic relationships between parts. This paper also introduce a Chain-of-Thought (CoT)  to causally  infer the relationship of parts. A new dataset, PM-X, is proposed to make a contribution to community.  Moreover,  this paper conduct extensive experiments to demonstrate the outperformance of the proposed method compared to prior arts.", "questions": "1. This paper should give more implementation details to help readers easily follow.\n2. More objects demos should be given to demonstrate the generalization.", "limitations": "This paper should discuss the limitations of the proposed method and the future work.", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strong:\n1. This paper propose  a 3D joint object generation framework based on image pairs of the resting state and the extended state. The dual-state image pairs offer important motion information. Compared with the single-image method, the dual-state input solves the fuzziness of component motion and spatial relationship. For multi-view methods, pair images are easy to obtain and cost less computation.\n2. To get more accurate relationships of parts, a Chain-of-Thought (CoT) tech is also introduced  to achieve better performance. \n3. This paper provides a new dataset, PM-X, which contains diverse and structurally complex joint objects, along with rendered images, URDF annotations, and language descriptions. I thinks the dataset has a great contribution to community.\n\nWeakness.\n1. The whole pipeline of this paper is complex, which relays several modules, such as diffusion model, COT and so on. And the implementation  details of this paper is not enough. So, I am afraid it is hard to reproduce the results of this proposed method.\n2. This paper just displays some demos about Cabinet.  So, I want to know how about this method perform on other objects. I think it is import to show the Generalization of this paper.", "was_revised": true, "final_justification": "I give 'accept' rate as final decision.", "rebuttal": "We sincerely thank Reviewer kNAa for their constructive and detailed feedback.\nWe greatly appreciate the reviewer’s recognition of the technical novelty of our proposed framework, the introduction of the PM-X dataset, and the use of Chain-of-Thought (CoT) reasoning. We also value the reviewer’s suggestions regarding reproducibility and generalization. Below, we provide clarifications and additional evidence to address the concerns raised.\n\n---\n### **Q1 Reproducibility Concerns**\n\nWe appreciate the reviewer’s concern regarding reproducibility. To facilitate understanding and verification, we provide demo code and pretrained model checkpoints in the supplementary material.\n\nUpon paper acceptance, we will release the full codebase, including all modules (diffusion, CoT-based graph reasoner, layout predictor), as well as the PM-X dataset, trained model weights, and detailed documentation covering implementation, prompt formats, training settings, and data preprocessing steps.\n\nWe are committed to ensuring the method is easy to reproduce and build upon.\n\n---\n\n### **Q2 Generalization Beyond Cabinet Category**\n\nAlthough the paper primarily displays *visual* results on cabinet-like objects, our experiments are conducted on **seven object categories** from the PartNet-Mobility dataset: `Storage Furniture`, `Table`, `Refrigerator`, `Dishwasher`, `Oven`, `Washer`, and `Microwave`. All reported quantitative results are evaluated **across these seven categories**. In addition, the ACD dataset provides a finer-grained categorization of object classes. The detailed mapping between PM and ACD categories used in our evaluation is shown below.\n\n**Correspondence Between PM Categories and ACD Categories**\n\n| **PM Category**      | **ACD Category** |\n| -------------------- | ---------------- |\n| StorageFurniture | HangingCabinet   |\n|                      | Cabinet          |\n|                      | Armoire          |\n|                      | ChestOfDrawers   |\n|                      | KitechenCabinet  |\n|                      | Bookcase         |\n|                      | TvStand          |\n|                      | SinkCabinet      |\n| Table            | Table            |\n|                      | Desk             |\n|                      | Nightstand       |\n| Refrigerator         | Refrigerator     |\n| Oven                 | Oven             |\n| Dishwasher           | Dishwasher       |\n| Washer               | Washer           |\n| Microwave            | Microwave        |\n\nWe chose to emphasize cabinet-type objects in the qualitative figures because they typically exhibit the most *structural complexity* and better demonstrate the advantages of our approach. However, we agree that including more visualizations from other categories would help illustrate the model’s generalization ability.\nWe will add such examples in the revised version to provide a more complete comparison.\n\nMoreover, we report quantitative results across all seven categories in the ACD dataset, as shown in the table below.\n\n**Comparison of DIPO and SINGAPO Across Categories and Metrics (↓ means lower is better)**\n\n| Category         | Method  | RS-\\$d\\_{gIoU}\\$ ↓ | AS-\\$d\\_{gIoU}\\$ ↓ | RS-\\$d\\_{cDist}\\$ ↓ | RS-\\$d\\_{cDist}\\$ ↓ | RS-\\$d\\_{CD}\\$ ↓    | AS-\\$d\\_{CD}\\$ ↓    |\n| ---------------- | ------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- |\n| StorageFurniture | SINGAPO | 1.0388     | 1.0421     | 0.1566     | 0.2087     | 0.0983     | 0.1700     |\n|                  | DIPO    | **0.9923** | **0.9946** | **0.1225** | **0.1508** | **0.0684** | **0.0948** |\n| Table            | SINGAPO | 0.9550     | 0.9583     | 0.1670     | **0.1876** | 0.1371     | 0.1628     |\n|                  | DIPO    | **0.8884** | **0.8960** | **0.1254** | 0.1903     | **0.0849** | **0.0971** |\n| Dishwaser        | SINGAPO | 0.5745     | 0.5703     | **0.0911** | **0.1367** | **0.0640** | 0.0958     |\n|                  | DIPO    | **0.4376** | **0.4299** | 0.1229     | 0.1822     | 0.0698     | **0.0948** |\n| Microwave        | SINGAPO | 0.9032     | 0.9027     | **0.1697** | **0.2015** | **0.1141** | 0.1968     |\n|                  | DIPO    | **0.9001** | **0.8999** | 0.1881     | 0.2092     | 0.1164     | **0.1940** |\n| Oven             | SINGAPO | 1.0938     | 1.0948     | 0.1858     | 0.2954     | 0.1949     | 0.2995     |\n|                  | DIPO    | **0.9838** | **0.9861** | **0.1703** | **0.2718** | **0.1584** | **0.2651** |\n| Refrigerator     | SINGAPO | 0.8309     | 0.8304     | **0.1461** | 0.1895     | **0.0881** | 0.1523     |\n|                  | DIPO    | **0.7560** | **0.7556** | 0.1571     | **0.1694** | 0.1161     | **0.1332** |\n| Washer           | SINGAPO | 0.4677     | 0.4732     | 0.1317     | 0.1721     | 0.0775     | 0.1650     |\n|                  | DIPO    | **0.3572** | **0.3645** | **0.0585** | **0.0825** | **0.0248** | **0.0271** |\n---"}]

Author rebuttal:

Meta-review: This paper introduces DIPO, a framework for generating articulated 3D objects from a pair of images, alongside a new dataset resource, PM-X. The review process for this paper began with a split decision, reflecting valid initial concerns about the work's generalization and the clarity of its contributions. However, an effective author rebuttal and a constructive discussion period successfully addressed these key issues, leading to a consensus for acceptance among the reviewers.

The final recommendation is to accept this paper for a poster presentation. The work presents a technically sound method for a challenging problem and offers a valuable new dataset to the community.

### Summary
The paper makes two primary contributions. The first is DIPO, a framework for controllable 3D articulated object generation. Its key idea is to use a dual-image input, one of the object in a resting state and another in an articulated state, to provide explicit motion cues. The framework uses a diffusion model to generate part layouts and a CoT-based graph reasoner to infer part connectivity. The second contribution is a new dataset of complex articulated objects, PM-X, which is generated via an automated pipeline called LEGO-Art. The authors claim that the DIPO framework significantly outperforms existing baselines and that the PM-X dataset enhances generalization to more complex object structures.

### Strengths
The reviewers converged on several key strengths:
- The use of a dual-state image pair is a simple but clever and effective idea. It provides rich motion information that is difficult to infer from a single image, yet remains a much lower data collection burden than requiring multi-view video.
- The overall architecture, which combines a diffusion model with a CoT-based reasoner, is well-motivated and technically solid for the task of articulated object generation.
- The introduction of the LEGO-Art pipeline and the resulting PM-X dataset is a significant contribution in its own right. It addresses a known limitation in the field—the lack of diverse and complex articulated object data—and serves as a valuable resource for future research.

### Weaknesses
The initial reviews raised several important weaknesses that made this a borderline case:
- The initial experiments were primarily focused on a few "cabinet-like" object categories (StorageFurniture, Table), raising valid concerns about whether the method could generalize to a wider variety of articulated objects.
- The role and value of the new PM-X dataset were not clearly demonstrated. It was used to supplement training data, but its utility as a standalone benchmark was not evaluated, making its significance hard to gauge.
- Some reviewers found the pipeline complex and certain experimental details, such as the performance metrics, to be unclear.

### Reasons for Recommendation
The recommendation for acceptance is based on the paper's solid dual contribution (a novel method and a new dataset) and, most importantly, the authors' highly effective response to the reviewers' initial concerns. The weaknesses, while valid, were shown to be addressable and were largely resolved during the rebuttal period.
The paper is recommended for a poster presentation. The combination of a novel application-focused method and a new dataset makes it a valuable addition to the conference.

### Summary of Discussion and Rebuttal
The review process began with a 2-2 split in ratings. The main points of contention were the method's generalization capabilities and the significance of the new PM-X dataset.
The authors' rebuttal was comprehensive:
- On Generalization: To address the concern about being limited to cabinet-like objects, the authors provided a new, detailed table showing their model's performance across seven diverse categories. These new results demonstrated that DIPO consistently outperforms the baseline across all categories, effectively refuting the initial criticism.
- On the PM-X Dataset: To clarify the dataset's contribution, the authors provided a new set of experiments evaluating their model's performance when trained with and without PM-X. The results clearly demonstrated that including PM-X leads to a significant performance improvement, validating its utility. They also created a standalone test set for PM-X to establish its potential as a future benchmark.
This targeted response successfully resolved the main concerns. Both of the initially more critical reviewers (r4pp and tw3w) acknowledged that their points had been addressed and consequently raised their scores to "Borderline Accept." This transformed a split decision into a consensus that the paper is a valuable contribution.
Decision: Accept (poster)