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arxiv: 2605.18010 · v1 · pith:UI2QCWC4new · submitted 2026-05-18 · 💻 cs.CV · cs.GR

Functionalization via Structure Completion and Motion Rectification

Mingrui Zhao , Sai Raj Kishore Perla , Kai Wang , Sauradip Nag , Duc Anh Nguyen , Jiayi Peng , Ruiqi Wang , Angel X. Chang
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Manolis Savva Ali Mahdavi-Amiri Hao Zhang
This is my paper

Pith reviewed 2026-05-20 12:20 UTC · model grok-4.3

classification 💻 cs.CV cs.GR
keywords object functionalizationfunctional graphgraph completionmotion rectification3D geometry realizationfurniture models3D asset repairphysical operability
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The pith

Object functionalization uses graph completion to add missing structures and rectify motions in 3D models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces object functionalization, a task that converts visually plausible but non-functional 3D models into ones that can operate physically. It frames the problem as completing a functional graph whose nodes represent object parts with motion attributes and whose edges capture functional and contact relations. A neural Graph Functionalizer called GraFu predicts the missing nodes and corrects edges. The completed graph then guides a geometry realization step that adds connectors and other elements in 3D, which also fixes erroneous motion annotations. Tests on furniture models show gains in collision and connectivity measures while matching existing motion prediction accuracy.

Core claim

Object functionalization is solved by representing a non-functional 3D object as an incomplete functional graph, completing that graph with a neural model to predict missing parts and relations, and using the completed graph to realize added 3D geometry while rectifying motions, with the result that the output models exhibit improved physical operability.

What carries the argument

The functional graph, a representation whose labeled nodes stand for object parts carrying motion attributes and whose labeled edges encode functional and contact relations, which is completed by the neural Graph Functionalizer to drive subsequent 3D geometry realization.

If this is right

  • The completed graph directly instantiates predicted connectors and structural elements as 3D geometry.
  • Erroneous human-annotated and predicted motions are rectified as a side effect of the geometry realization stage.
  • Motion prediction accuracy matches state-of-the-art methods on PartNet-Mobility zero-shot and HSSD test sets.
  • Functionality improves substantially on collision and connectivity metrics for furniture models.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same graph-completion approach could be applied to 3D models from other categories such as tools or mechanical assemblies without retraining on furniture-specific data.
  • Large-scale automated production of functional 3D assets becomes feasible once the graph completion step is integrated with existing generative pipelines.
  • Physics-based feedback loops could be added to the geometry realization stage to further reduce residual collisions after graph completion.

Load-bearing premise

Structural and functional deficiencies in a 3D model can be fully captured as missing nodes or wrong edges in a labeled functional graph, so that completing the graph is enough to produce correct 3D elements and fixed motions.

What would settle it

A test in a physics simulator that applies the predicted motions to the functionalized models and measures whether collisions and structural failures are eliminated compared with the original non-functional versions.

Figures

Figures reproduced from arXiv: 2605.18010 by Ali Mahdavi-Amiri, Angel X. Chang, Duc Anh Nguyen, Hao Zhang, Jiayi Peng, Kai Wang, Manolis Savva, Mingrui Zhao, Ruiqi Wang, Sai Raj Kishore Perla, Sauradip Nag.

Figure 1
Figure 1. Figure 1: Our method functionalizes a cabinet model that cannot function as it should, due to a) missing connectors for its door (hinge) or drawer (rails) to open and be held in place, and b) a collision between the door and the side panel. Our solution pipeline consists of a neural graph functionalizer (GraFu) for functional graph completion, e.g., adding missing connectors (new graph edges), as well as drawer hand… view at source ↗
Figure 2
Figure 2. Figure 2: Our Pipeline. Given a non-functional 3D furniture asset, the system encodes each part node using category, point cloud, and bounding box centroid features. A Graph Transformer then models inter-node relationships across the fully-connected graph. The resulting node features are passed to a DETR-style Slot Decoder, which predicts per-slot attributes (category, bounding box, motion axis) and per-slot-pair ed… view at source ↗
Figure 3
Figure 3. Figure 3: Various application conditions for Contact-face snapping, Flushing [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Limitations and failure cases. Our method is bounded by the available [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative results and comparisons to Particulate and SINGAPO on test samples from PN-M (top 7 rows) and HSSD (bottom 2 rows). For our results, [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: A gallery of qualitative results from our object functionalization tool. Dynamic functionalization is shown in (a-g), where in each, the top is before [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Dynamic and static functionalizations applied to a 3D model obtained from single-view 3D reconstruction by Omniparts [Yang et al. 2025]. [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Mechanical part annotation. Row 1: interior mount hinge with non [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: 100 randomly sampled PN-M models with missing tops. Top row, GT render, bottom row: ours functionalized. Please zoom in for details. [PITH_FULL_IMAGE:figures/full_fig_p016_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Comparison against BlenderMCP. Left: BlenderMCP, right: Ours. (a) Adding rails to the drawer. (b)(d) Adding hinges to the cabinet. (c) Adding [PITH_FULL_IMAGE:figures/full_fig_p018_10.png] view at source ↗
read the original abstract

Acquisition and creation of 3D assets have been largely view- or appearance-driven. As a result, existing digital 3D models often lack the requisite structural components to function as intended, such as joints, supports, interiors, or interaction elements. At the same time, even human-annotated motions are frequently error-prone, leading to physically implausible behavior. We introduce object functionalization, a novel task aimed at transforming visually plausible but non-functional 3D models into functional and physically operable ones. We formulate functionalization as a graph completion problem over a new functional graph representation, where labeled nodes represent object parts, labeled edges encode functional and contact relations, and movable nodes carry motion attributes, so that structural functional deficiencies manifest as missing nodes or incorrect edges. We develop a neural Graph Functionalizer (GraFu) to complete an incomplete graph representing a non-functional 3D object. The completed graph then drives a geometry realization stage that instantiates predicted connectors and structural elements in 3D, with the compelling side effect of rectifying erroneous human-annotated and predicted motions. To support training and evaluation, focusing on furniture as a rich and challenging target category, we introduce FurFun-233, a dataset of 233 paired non-functional and functionalized furniture models. On PartNet-Mobility ("zero-shot") and HSSD test sets, our method matches state-of-the-art methods in motion prediction accuracy while substantially improving functionality in terms of collision and connectivity.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces the task of object functionalization to convert visually plausible but non-functional 3D models into physically operable ones. It formulates the problem as graph completion over a novel functional graph representation (labeled nodes for parts, edges for functional/contact relations, and motion attributes on movable nodes). A neural Graph Functionalizer (GraFu) completes the incomplete input graph; the completed graph then drives a geometry realization stage that instantiates predicted connectors and structural elements in 3D, with the side effect of rectifying erroneous motions. The authors introduce the FurFun-233 paired furniture dataset and report that the method matches SOTA motion-prediction accuracy on zero-shot PartNet-Mobility and HSSD evaluations while substantially improving collision and connectivity functionality metrics.

Significance. If the central claims hold, the work is significant because it directly targets the gap between appearance-driven 3D asset creation and physical operability, which is relevant for robotics, simulation, and interactive applications. The graph-completion framing and the incidental motion-rectification effect are conceptually clean; the release of FurFun-233 provides a concrete benchmark for future work. These elements would strengthen the paper's contribution provided the geometric validity of the realization stage is rigorously demonstrated.

major comments (2)
  1. [§4.2] §4.2 (Geometry Realization): The central claim that graph completion is sufficient to produce collision-free, physically operable 3D models rests on the unstated assumption that every completed edge relation admits a unique, stable 3D embedding. The manuscript does not provide a constraint solver or disambiguation procedure; if realization uses heuristic placement, small errors in predicted edges could still produce intersecting geometry or violated joint limits, undermining the reported collision/connectivity gains.
  2. [§5.3] §5.3 and Table 3: The zero-shot results on PartNet-Mobility and HSSD claim improved functionality metrics, yet no ablation isolates the contribution of graph completion versus the downstream realization heuristics. Without such controls, it is unclear whether the functionality improvements are robust to the discrete abstraction or merely artifacts of the particular instantiation procedure.
minor comments (2)
  1. The abstract would be strengthened by including one or two key quantitative numbers (e.g., collision-rate reduction or connectivity score) rather than qualitative statements of improvement.
  2. [§3.1] Notation for motion attributes on movable nodes should be defined explicitly in §3.1 to avoid ambiguity when readers compare the functional graph to standard scene graphs.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their insightful comments on our manuscript. We are pleased that the referee recognizes the significance of the object functionalization task and the introduction of the FurFun-233 dataset. We address each major comment below, providing clarifications and outlining revisions to strengthen the paper.

read point-by-point responses

  1. Referee: [§4.2] §4.2 (Geometry Realization): The central claim that graph completion is sufficient to produce collision-free, physically operable 3D models rests on the unstated assumption that every completed edge relation admits a unique, stable 3D embedding. The manuscript does not provide a constraint solver or disambiguation procedure; if realization uses heuristic placement, small errors in predicted edges could still produce intersecting geometry or violated joint limits, undermining the reported collision/connectivity gains.

    Authors: We thank the referee for highlighting this important aspect of the geometry realization stage. The realization procedure is indeed heuristic in nature, using the completed functional graph to determine attachment points, orientations, and structural additions based on the predicted relations and motion attributes. Specifically, contact edges define spatial constraints for part placement, while motion attributes on nodes specify joint axes and limits that are enforced during instantiation. Although we do not employ a general-purpose constraint solver, the graph structure ensures that the embedding is consistent with the functional specifications by design. Our quantitative results demonstrate that this approach leads to measurable improvements in collision avoidance and connectivity, indicating practical stability for the furniture category. To address the concern, we will expand §4.2 with a detailed description of the instantiation algorithm, including how potential ambiguities are resolved through priority rules derived from the graph labels. This will make the assumptions more explicit. revision: partial

  2. Referee: [§5.3] §5.3 and Table 3: The zero-shot results on PartNet-Mobility and HSSD claim improved functionality metrics, yet no ablation isolates the contribution of graph completion versus the downstream realization heuristics. Without such controls, it is unclear whether the functionality improvements are robust to the discrete abstraction or merely artifacts of the particular instantiation procedure.

    Authors: We agree that an ablation isolating the graph completion from the realization would strengthen the claims. The realization stage is deterministic given the input graph, and we apply the same procedure to both our completed graphs and those from baseline methods where applicable. The functionality gains are tied to the accuracy of the completed functional relations, as poorer graph predictions lead to more collisions in our tests. In the revised version, we will add an ablation in §5.3 that evaluates functionality metrics using the realization on (i) ground-truth graphs, (ii) our predicted graphs, and (iii) graphs from alternative completion approaches, to demonstrate that the improvements stem from better graph completion rather than the realization heuristics alone. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation is self-contained

full rationale

The paper formulates functionalization as a graph completion problem on a functional graph (nodes for parts, edges for relations, motion attributes on movable nodes), trains a neural Graph Functionalizer (GraFu) to complete incomplete graphs from non-functional inputs, and then applies a separate geometry realization stage to instantiate connectors and rectify motions. This chain does not reduce any claimed output to its inputs by construction: graph completion is a learned prediction from data, realization is a downstream geometric process, and evaluations on FurFun-233, PartNet-Mobility, and HSSD are external. No equations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the provided text that would create definitional equivalence. The approach is therefore independently verifiable against the reported metrics.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the premise that functional deficiencies are expressible as graph incompleteness and that graph completion plus geometry realization yields physically operable models; no free parameters or invented physical entities are named in the abstract.

axioms (1)
  • domain assumption Structural and functional deficiencies of a 3D object manifest as missing nodes or incorrect edges in a functional graph
    Explicitly stated in the abstract as the modeling choice that turns functionalization into a graph-completion problem.
invented entities (1)
  • functional graph representation no independent evidence
    purpose: To encode parts, functional/contact relations, and motion attributes so that deficiencies appear as missing or wrong graph elements
    New representation introduced to support the graph-completion formulation; no independent evidence outside the paper is provided in the abstract.

pith-pipeline@v0.9.0 · 5827 in / 1441 out tokens · 37410 ms · 2026-05-20T12:20:32.036377+00:00 · methodology

discussion (0)

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