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StackGen3D: Stable 3D Block Stack Generation from Multiview Silhouettes

Summary

StackGen3D is a new arXiv framework that generates physically stable block assemblies conditioned on a given block inventory and three orthographic silhouettes. It first trains a physics-informed graph neural network (PI-GNN) to model support relations between blocks and act as a fast stability predictor for curating a diverse dataset of stable structures, then trains a conditional flow-matching model on that dataset to capture the multimodal distribution of block configurations consistent with the target silhouettes. At inference, lightweight geometric selection and refinement reduce inter-block penetration without physics simulation. Best-of-10 selection improves physical stability from 45.3% to 72.4% while achieving a silhouette IoU of 0.970.

SourcearXiv Computer VisionAuthor: Xiaopeng Zhang, Luzhe Sun, Yiqing Xu, Matthew R. Walter
StackGen3D: Stable 3D Block Stack Generation from Multiview Silhouettes
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[Submitted on 22 Oct 2026]

Title:StackGen3D: Stable 3D Block Stack Generation from Multiview Silhouettes

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Abstract:Generating physically stable 3D structures from visual specifications requires resolving geometric ambiguity while accounting for physical interactions among objects. We present StackGen3D, a framework for generating stable block assemblies conditioned on a given block inventory and three orthographic silhouettes. Our approach consists of two stages. First, we develop a physics-informed graph neural network (PI-GNN) that models support relations between blocks and serves as a fast stability predictor for curating a diverse dataset of stable structures. Second, we train a conditional flow-matching model on this dataset to capture the multimodal distribution of block configurations consistent with the target silhouettes. At inference time, lightweight geometric selection and refinement reduce inter-block penetration without physics simulation. Preliminary experiments show that the PI-GNN generalizes from simple stacks to complex structures substantially better than a parameter-matched multilayer perceptron. For structure generation, Best-of-10 selection improves physical stability from 45.3% to 72.4% while achieving a silhouette IoU of 0.970. These results highlight the potential of combining relational physical reasoning, scalable data curation, and conditional generative modeling for creative 3D assembly.

Project website: [this https URL](this https URL)

Comments: A poster of this work is publicly available on the website of the Creativity in Assembly Workshop at IROS 2026 (non-archival)

Subjects:

Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)

Cite as: arXiv:2610.31803 [cs.CV]

(or arXiv:2610.31803v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2610.31803

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Luzhe Sun [view email] [v1] Thu, 22 Oct 2026 18:41:21 UTC (1,575 KB)

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Key points and analysis

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Key points

  • Two-stage pipeline: a physics-informed graph neural network (PI-GNN) predicts block support relations and curates a dataset of stable structures, then a conditional flow-matching model learns to generate silhouette-consistent block configurations.
  • Inference uses lightweight geometric selection and refinement to reduce inter-block penetration without running physics simulation.
  • PI-GNN generalizes from simple stacks to complex structures substantially better than a parameter-matched multilayer perceptron.
  • Best-of-10 selection raises physical stability from 45.3% to 72.4% while maintaining a silhouette IoU of 0.970.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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