mlsanityData to 7 Oct 2026

Printing the Underdetermined: Materializing Multi-solutionness in Figurative Paintings

arXiv 2609.19782 ↗First posted 17 Sep 2026cs.GR cs.CVcited by 0

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Abstract (arXiv)

Figurative paintings are often approached as if they depict a single recoverable 3D scene: viewers infer depth and occlusion, and reconstruction pipelines attempt to converge to one stable model. We instead foreground multi-solutionness, the non-uniqueness of 3D configurations compatible with a single painted image, and propose a workflow that keeps this non-uniqueness visible and material. Multi-solutionness arises from two sources: unobserved content, where backsides and occluded volumes admit multiple plausible completions, and observed cues, where perspective, shading, and occlusion still underconstrain geometry. When additional views are synthesized by a video generative model without explicit 3D constraints, small frame-level drifts become inevitable rather than exceptional. Our pipeline samples multiple camera-orbit multi-view video sequences from one painting, reconstructs each sequence with 3D Gaussian Splatting into a point-based Gaussian scene representation where density halos and ghosting expose unresolved degrees of freedom, and fabricates these representations as physical artifacts using DreamPrinting. By treating multiple compatible interpretations as explicit outputs rather than residual error, we provide a computational framework for spatial readings of figurative painting that can be inspected, compared, and discussed in both digital and physical form.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 39 references that are in the map. First posted on arXiv 17 Sep 2026.

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16 of 39 references.

References · 16

  1. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
  2. 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,006
  3. 2025Wan: Open and Advanced Large-Scale Video Generative Modelscited by 2,536
  4. 2023Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasetscited by 2,526
  5. 2023Zero-1-to-3: Zero-shot One Image to 3D Objectcited by 1,483
  6. 2022Magic3D: High-Resolution Text-to-3D Content Creationcited by 1,285
  7. 2023ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillationcited by 1,207
  8. 2024WorldSimBench: Towards Video Generation Models as World Simulatorscited by 867
  9. 2024CLAY: A Controllable Large-scale Generative Model for Creating High-quality 3D Assetscited by 444
  10. 2024TripoSR: Fast 3D Object Reconstruction from a Single Imagecited by 227
  11. 2025WorldPlay: Towards Long-Term Geometric Consistency for Real-Time Interactive World Modelingcited by 152
  12. 2022ActiveNeRF: Learning where to See with Uncertainty Estimationcited by 124
4 more
  1. 2024Hunyuan3D 1.0: A Unified Framework for Text-to-3D and Image-to-3D Generationcited by 82
  2. 2025MatAnyone: Stable Video Matting with Consistent Memory Propagationcited by 24
  3. 2022ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Imagescited by 12
  4. 2025DreamPrinting: Volumetric Printing Primitives for High-Fidelity 3D Printingcited by 1

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