ArticuTable: Generating Instance-Level Interactive Rigid-Articulated 3D Tabletop Scenes from a Single Image

Kai Lv; Yibo Yin; Lijun Guo; Heng Fan; Kaihao Zhang; Xingping Dong

cs.CVOct 4, 2026

ArticuTable: Generating Instance-Level Interactive Rigid-Articulated 3D Tabletop Scenes from a Single Image

Authors: Kai Lv, Yibo Yin, Lijun Guo, Heng Fan, Kaihao Zhang, Xingping Dong

Organizations: School of Computer Science, Wuhan University · Ecole Polytechnique F´ed´erale de Lausanne (EPFL), Switzerland · Department of Computer Science and Engineering, University of North Texas · Australian National University

Abstract

Embodied agents benefit from 3D environments that combine visual fidelity to real-world observations with physical interactivity. Existing single-image tabletop reconstruction methods recover plausible scene geometry but typically represent objects as monolithic rigid bodies, limiting interaction to whole-object rigid motion and precluding executable part-level articulation. Meanwhile, recovering a scene layout consistent with the input view remains challenging because a single observation may admit multiple plausible pose-scale configurations. We present ArticuTable, a single-image 3D tabletop reconstruction framework that recovers both executable part-level articulation and an input-view-consistent scene layout. For object modeling, we introduce generation-robust articulation modeling (GRAM), which combines joint fitting guided by a multimodal large language model with semantic state reasoning to recover reliable joint parameters and valid motion ranges from imperfect monolithic proxy meshes, thereby converting them into executable articulated assets. For scene layout, we introduce progressive semantic-geometric scene registration (PSGSR), which progressively narrows the pose-scale search space under complementary metric, planar, and input-view constraints and resolves orientation ambiguity through structure-aware semantic correspondences, yielding a scene layout consistent with the input view. We further contribute ArticuTable-100, a curated collection of 100 simulation-ready tabletop scenes. Extensive evaluation, including a user study, demonstrates strong performance across visual fidelity, input-view consistency, articulation quality, physical plausibility, and simulation readiness.

Figures & tables

Figure 1: Single-image reconstruction of articulated tabletop scenes. Given one RGB image (left), ArticuTable reconstructs an input-view-consistent 3D scene containing rigid and articulated objects. Renderings of the same scene under different joint configurations (right) demonstrate part-level articulation while preserving the arrangement of object instances.

Figure 2: Overview of ArticuTable. From a single RGB tabletop image, ArticuTable reconstructs object-wise 3D proxies, converts operable proxies into executable URDF assets with GRAM, and registers them into a simulation-ready scene with PSGSR.

Figure 3: Qualitative comparison of scene-level reconstruction and registration on generated and real-world inputs.

	Input-View Consistency			GPT-5 Evaluation					Physical Validity
Method	LPIPS $\downarrow$	DINOv2 $\uparrow$	CLIP $\uparrow$	VF $\uparrow$	IA $\uparrow$	PP $\uparrow$	Avg. $\uparrow$	OR $\downarrow$	$\mathrm{Col}_{O}$ (%) $\downarrow$	$\mathrm{Col}_{S}$ (%) $\downarrow$
ACDC ( Dai et al., 2025 )	0.4447	0.5135	0.8058	3.927	1.920	3.880	3.242	4.298	1.5302	32.00
Gen3DSR ( Ardelean et al., 2025 )	0.2716	0.5704	0.8386	2.220	4.680	4.013	3.638	3.349	11.3322	66.67
MIDI ( Huang et al., 2025 )	0.4302	0.6469	0.8668	2.787	2.933	2.360	2.693	4.142	37.9430	99.33
TabletopGen ( Wang et al., 2026 )	0.3935	0.7788	0.8908	5.380	4.860	5.893	5.378	2.162	1.4488	16.00
ArticuTable (Ours)	0.2398	0.8991	0.9265	5.867	6.253	6.600	6.240	1.049	0.3819	10.67

Table 1: Scene-level results. Avg. is the mean of VF, IA, and PP, and OR is the mean overall rank. Best and second-best values are bolded and underlined.

Method	Input	Task-Training-Free	Prec. $\uparrow$	Rec. $\uparrow$	Rest mIoU $\uparrow$	Art. gIoU $\uparrow$	OC $\downarrow$	AE ( ∘ ) $\downarrow$	LE $\downarrow$
SINGAPO ( Liu et al., 2025 )	Image	–	37.60	25.40	0.182	-0.263	0.030	30.30	0.077
PAct ( Liu et al., 2026 )	Image	–	24.60	16.80	0.092	-0.434	0.056	42.90	0.188
PhysX-Anything ( Cao et al., 2025 )	Image	–	20.30	19.20	0.093	-0.459	0.064	38.80	0.123
URDF-Anything+ ( Wu et al., 2026 )	Image	–	70.70	23.90	0.260	-0.138	0.033	50.40	0.128
Articulate AnyMesh ( Qiu et al., 2025 )	Mesh	$\checkmark$	89.20	42.50	0.452	0.158	0.010	21.70	0.043
URDF-Anything+ ( Wu et al., 2026 )	Mesh	–	77.50	27.40	0.267	-0.110	0.026	49.90	0.111

Table 2: Results on the Lightwheel articulation benchmark ( Li et al., 2026b ) . Best and second-best results within each input modality are bolded and underlined, respectively.

Figure 4: Robustness on raw image-to-3D meshes. Particulate versus GRAM across motion states.

Component	Metric	Enabled	Disabled	Reduction
MLLM-Guided Joint Fitting	AE $\downarrow$	$\mathbf{17.17}^{\circ}$	$40.06^{\circ}$	57.1%
MLLM-Guided Joint Fitting	LE $\downarrow$	0.0417	0.0527	20.9%
Semantic State Reasoning	Art. PC $\downarrow$	0.2502	0.2605	3.9%
Semantic State Reasoning	OC $\downarrow$	0.0152	0.0176	13.6%

Table 3: GRAM component ablation on Lightwheel. Enabled and disabled values are averaged over both settings of the other component; lower is better.

Variant	Top-view Stage	Input-view Stage	SASPS	LPIPS $\downarrow$	DINOv2 $\uparrow$	CLIP $\uparrow$
Point-cloud alignment only	–	–	–	0.3439	0.8102	0.9077
w/o Top-view stage	$\times$	$\checkmark$	–	0.2564	0.8702	0.9210
Top-view stage only	$\checkmark$	$\times$	$\times$	0.3235	0.8586	0.9156
w/o SASPS	$\checkmark$	$\checkmark$	$\times$	0.2543	0.8762	0.9205
Full model	$\checkmark$	$\checkmark$	$\checkmark$	0.2398	0.8991	0.9265

Table 4: PSGSR ablation. “–” denotes a component not applicable to the corresponding variant.

Figure 5: Qualitative pose-selection ablation. Red boxes indicate representative orientation errors.

Metric	ACDC	Gen3DSR	MIDI	TabletopGen	ArticuTable
Mean Rank $\downarrow$	4.08	3.75	3.75	2.24	1.17
Median Rank $\downarrow$	4	4	4	2	1
First-Place Rate $\uparrow$	0.17%	1.02%	1.02%	12.20%	85.59%
Top-2 Rate $\uparrow$	6.61%	11.86%	8.81%	74.58%	98.14%

Table 5: User-study rankings from 59 participants. Each method receives 590 valid rankings; best and second-best results are bolded and underlined.

Figure 6: Scene editing and physical interaction in Isaac Sim. Top: object removal, insertion, and rearrangement. Bottom: gravity settling, contact-based transport, and articulated opening. Yellow dashed ellipses highlight the edited or manipulated regions.

Figure 7: Residual projection misalignment. Despite the overall scene alignment, the reconstructed cup exhibits less projected tilt than the input cup. Red boxes identify the cups; cyan dashed lines indicate their projected axes, and green dashed lines indicate the image vertical.

Appendix figures & tables8 assets

Supplementary material from the paper’s appendix.

Appendix

Evaluation level	$N$	Pass	Partial	Fail
Predicted joint	527	454 (86.1%)	57 (10.8%)	16 (3.0%)
Asset	418	346 (82.8%)	55 (13.2%)	17 (4.1%)

Appendix

Table 6: Manual audit of generated articulations over the complete 150-scene evaluation set. Joint-level results include predicted joints only. Asset-level percentages are computed over the 418 judgeable assets; seven unjudgeable assets are excluded. Clear missing articulations are counted as asset-level failures.

Measure	$N$	Exact agreement	Cohen’s $\kappa$
Asset-level result	85	83.5%	0.476
Structure score	101	93.1%	0.474 †
Motion score	101	89.1%	0.659 †

Appendix

Table 7: Inter-rater agreement on the random 20% re-evaluation sample. The dagger denotes quadratically weighted Cohen’s

\kappa

Configuration	AE ( ∘ ) $\downarrow$	LE $\downarrow$	Art. PC $\downarrow$	OC $\downarrow$
Neither component	40.06	0.0527	0.2556	0.0177
MLLM-Guided Joint Fitting only	17.17	0.0417	0.2653	0.0175
Semantic State Reasoning only	40.06	0.0527	0.2552	0.0149
Both components	17.17	0.0417	0.2452	0.0154

Appendix

Table 8: Full GRAM component ablation on Lightwheel. Lower is better for all metrics.

Figure 8: User-study interface. The website interface used to present method outputs and collect participant rankings.

Figure 9: Additional qualitative results of articulated tabletop-scene reconstruction. The first column shows the input images, while the remaining columns show the corresponding reconstructed scenes under different joint configurations.

Stage	Time (s) $\downarrow$
Table and camera initialization	$8.3\pm 2.4$
Point-cloud alignment	$14.1\pm 2.0$
Top-view registration	$15.7\pm 2.6$
Input-view registration	$103.3\pm 11.3$
Total PSGSR	$\mathbf{141.4\pm 14.0}$

Appendix

Table 9: PSGSR runtime. Per-scene wall-clock time over 150 scenes, reported as mean

\pm

standard deviation.

Variant	Initialization	Candidate Rendering	Time (s) $\downarrow$	Relative
Full PSGSR	Top-view-guided	Batched	179.4	$\mathbf{1.00\times}$
Front-only SASPS	Input-view only	Batched	374.8	$2.09\times$
Front-only SASPS	Input-view only	Sequential	595.3	$3.32\times$

Appendix

Table 10: Pose-selection efficiency. Runtime averaged over the two scenes in Fig. 5 .

Setting	Configuration
Package format	Self-contained USDZ
Stage units	$1$ unit $=1$ m
Up axis	$+Z$
Gravity	$9.81~\mathrm{m/s^{2}}$ along $-Z$
Visual representation	Original geometry with PBR materials and textures
Static table collision	Original unsimplified triangle mesh

Appendix

Table 11: Configuration used to export the simulation-ready USDZ packages.

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