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arxiv: 2605.14399 · v1 · submitted 2026-05-14 · 💻 cs.CV · cs.GR

Recognition: no theorem link

SceneForge: Structured World Supervision from 3D Interventions

Jizhizi Li , Jiayang Ao , Danny Wicks , Petru-Daniel Tudosiu
Authors on Pith no claims yet

Pith reviewed 2026-05-15 01:30 UTC · model grok-4.3

classification 💻 cs.CV cs.GR
keywords scene supervision3D interventionscounterfactual generationobject removalscene removalstructured world statesmultimodal learninggeometric dependencies
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The pith

SceneForge generates consistent supervision for removal tasks by propagating explicit interventions through editable 3D scene states.

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

SceneForge tackles the problem of obtaining supervision that stays consistent across edits and viewpoints by shifting from image-only data to editable 3D world representations. Each scene is treated as a persistent state holding semantic, geometric, and physical links; interventions such as object removal are applied directly to this state and their consequences are rendered out as aligned outputs including counterfactual views, multi-view images, and signals like shadows. The method produces a large collection of such pairs and annotations across thousands of scenes in both single-view and registered multi-view formats. When added to training under fixed budgets, the resulting supervision raises performance on object removal and scene removal benchmarks in both numbers and visual quality. A reader would care because the approach offers a route to labels that respect scene structure without relying on post-hoc image adjustments.

Core claim

SceneForge is an intervention-driven framework that represents each scene as a persistent world containing semantic, geometric, and physical dependencies. Explicit interventions are applied to this shared state and their effects are propagated to produce aligned supervision signals such as counterfactual observations, multi-view renderings, and effect-aware outputs like shadows and reflections. The framework yields a licensing-clean indoor resource containing large numbers of counterfactual pairs and annotations from over 2K scenes, supporting both single-view and registered multi-view settings, and yields measurable gains in removal tasks when used under matched training budgets.

What carries the argument

SceneForge, an intervention-driven framework that models scenes as persistent worlds with dependencies and propagates explicit edits through those dependencies to render consistent supervision.

If this is right

  • Incorporating SceneForge supervision raises quantitative and qualitative performance on object removal and scene removal across multiple benchmarks under matched training budgets.
  • Supervision remains consistent with object structure and scene-level effects because it derives from a shared world state rather than image-space processing.
  • The approach supplies aligned counterfactual observations, multi-view observations, and effect-aware signals such as shadows and reflections.
  • The resulting resource covers diverse single-view and registered multi-view settings from over 2K scenes and provides a scalable foundation for intervention-consistent multimodal learning.

Where Pith is reading between the lines

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

  • The same intervention-propagation mechanism could support additional tasks that require labels stable under edits, such as novel-view synthesis or physical property prediction.
  • Practical use would hinge on measuring how much domain gap remains between the generated supervision and real captured images.
  • If the dependency modeling holds, similar world-state supervision could reduce reliance on large-scale manual annotation for consistency-critical vision problems.

Load-bearing premise

That supervision generated from synthetic 3D interventions transfers effectively to real-world images and that the modeled scene dependencies accurately reflect real physical and geometric relationships.

What would settle it

A controlled experiment in which models trained with SceneForge supervision show no improvement over matched baselines when evaluated on held-out real images with ground-truth removal masks would falsify the central performance claim.

Figures

Figures reproduced from arXiv: 2605.14399 by Danny Wicks, Jiayang Ao, Jizhizi Li, Petru-Daniel Tudosiu.

Figure 1
Figure 1. Figure 1: Overview of the SceneForge pipeline. Editable 3D scenes are first acquired or generated [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Object-linked layer decomposition in an Infinigen scene. From one scene state, SceneForge [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Visual-effect-aware layer decomposition and recomposition in Blender scenes. Rows [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Single-camera counterfactual examples from [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Registered multi-camera counterfactual strips from [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Hard-case examples from Removal-HardEffects. Within each example, columns from left to right show the original image, mask, OpenData-30K result, and SceneForge-16K result. These qualitative comparisons show that SceneForge-16K more reliably removes associated shadows, reflections, and other local side effects during object removal [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Subjective results on realistic real-world cases using the model trained on [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 4
Figure 4. Figure 4: Figure 11 provides additional examples together with the amodal RGBA ground truth of [PITH_FULL_IMAGE:figures/full_fig_p014_4.png] view at source ↗
Figure 8
Figure 8. Figure 8: Three additional layer decomposition examples. Each example is shown without cropping [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Three further layer decomposition examples. These cases further show that the same [PITH_FULL_IMAGE:figures/full_fig_p017_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Additional visual-effect-aware decomposition examples complementary to Figure 3. [PITH_FULL_IMAGE:figures/full_fig_p018_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Additional counterfactual examples from SceneForge-Removal (S). Columns show the original image, removal mask, counterfactual image after removal, and aligned RGBA amodal layers of the removed object derived from the same world state. For partially occluded objects (left), the amodal layers recover occluded object regions, while for fully visible objects (right), the RGBA layers coincide with the visible … view at source ↗
Figure 12
Figure 12. Figure 12: Examples of synchronized counterfactual video observations generated from the same [PITH_FULL_IMAGE:figures/full_fig_p019_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Nine additional hard-case qualitative examples from [PITH_FULL_IMAGE:figures/full_fig_p020_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Additional RemovalBench qualitative results. Each row shows one example at full width. From left to right, columns show the original image, mask, ground-truth target, OpenData-30K, OpenData+SceneForge-30K, and SceneForge-16K. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_14.png] view at source ↗
read the original abstract

Many multimodal learning tasks require supervision that remains consistent across edits, viewpoints, and scene-level interventions. However, such supervision is difficult to obtain from observation-level datasets, which do not expose the underlying scene state or how changes propagate through it. We present SceneForge, an intervention-driven framework that generates structured supervision from editable 3D world states. SceneForge represents each scene as a persistent world with semantic, geometric, and physical dependencies. By applying explicit interventions (e.g., object removal or camera variation) and propagating their effects through scene dependencies, SceneForge renders supervision that remains consistent with object structure and scene-level effects. This produces aligned outputs including counterfactual observations, multi-view observations, and effect-aware signals such as shadows and reflections, all derived from a shared world state rather than post hoc image-space processing. We instantiate SceneForge using Infinigen and Blender to construct a licensing-clean indoor supervision resource with a large number of counterfactual pairs and aligned annotations from over 2K scenes, covering both diverse single-view and registered multi-view settings. Under matched training budgets, incorporating SceneForge supervision improves both object removal and scene removal performance across multiple benchmarks in both quantitative and qualitative evaluation. These results indicate that modeling supervision as structured state transitions in editable worlds provides a practical and scalable foundation for intervention-consistent multimodal learning.

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 SceneForge, an intervention-driven framework that models scenes as persistent 3D worlds with semantic, geometric, and physical dependencies. Using Infinigen and Blender, it generates a licensing-clean indoor dataset from over 2K scenes containing counterfactual pairs, multi-view renders, and effect-aware signals (shadows, reflections). The central claim is that incorporating this structured supervision improves object removal and scene removal performance across multiple benchmarks under matched training budgets, with gains shown in both quantitative and qualitative evaluations.

Significance. If the empirical results hold, the work supplies a scalable route to supervision that respects scene-level state transitions rather than relying on post-hoc image edits. This could benefit multimodal tasks requiring edit consistency, such as inpainting or novel-view synthesis. The construction of a large, editable 3D supervision resource is a concrete asset that future work can build upon.

major comments (2)
  1. [Abstract and Experimental Results] The headline claim of performance gains on real or mixed benchmarks under matched budgets is load-bearing, yet the abstract supplies no numerical results, baselines, error bars, or ablation details. The experimental section must include these to allow assessment of whether gains arise from intervention consistency rather than increased data volume or regularization.
  2. [Method and Experiments] The synthetic-to-real transfer assumption is central: supervision generated from Infinigen/Blender interventions must accurately propagate real physical and geometric effects. No direct validation (real-image intervention ground truth, cross-domain ablation, or fidelity metrics) is described to confirm that modeled dependencies match actual scene physics rather than synthetic artifacts.
minor comments (2)
  1. [Abstract] The abstract refers to 'multiple benchmarks' without naming them; an explicit list would improve readability.
  2. [Dataset Construction] Clarify the precise count of counterfactual pairs, the distribution of intervention types, and how multi-view registration is performed across the 2K scenes.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment point by point below, with proposed revisions to improve clarity and completeness.

read point-by-point responses

  1. Referee: [Abstract and Experimental Results] The headline claim of performance gains on real or mixed benchmarks under matched budgets is load-bearing, yet the abstract supplies no numerical results, baselines, error bars, or ablation details. The experimental section must include these to allow assessment of whether gains arise from intervention consistency rather than increased data volume or regularization.

    Authors: We agree that the abstract would benefit from explicit numerical results to support the claims. In the revised version, we will incorporate key quantitative findings from the experiments, including specific performance improvements on object removal and scene removal benchmarks under matched training budgets, along with references to the relevant baselines and ablations. The experimental section already contains ablations that control for data volume and regularization effects by comparing against non-intervention baselines with equivalent data quantities; we will ensure these are clearly highlighted with error bars from repeated runs to demonstrate that gains stem from intervention consistency. revision: yes

  2. Referee: [Method and Experiments] The synthetic-to-real transfer assumption is central: supervision generated from Infinigen/Blender interventions must accurately propagate real physical and geometric effects. No direct validation (real-image intervention ground truth, cross-domain ablation, or fidelity metrics) is described to confirm that modeled dependencies match actual scene physics rather than synthetic artifacts.

    Authors: We acknowledge the value of direct validation for the synthetic-to-real transfer. While we do not provide paired real-image intervention ground truth (due to the inherent difficulty of obtaining such data), our primary evaluations are conducted on real-world benchmarks, where the addition of SceneForge supervision yields measurable gains. This provides indirect evidence of effective transfer. In revision, we will add a dedicated discussion subsection on effect fidelity, including qualitative comparisons of rendered physical effects (e.g., shadows and reflections) against real scenes, and expand cross-domain analysis where possible. We will also explicitly note the absence of direct real intervention validation as a limitation. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical framework with external benchmarks

full rationale

The paper introduces SceneForge as an intervention-driven supervision generator instantiated via external tools (Infinigen, Blender) on 2K+ scenes, then reports measured performance gains on object/scene removal benchmarks under matched training budgets. No equations, fitted parameters, or predictions are presented that reduce to inputs by construction. No self-citation chains or uniqueness theorems are invoked as load-bearing premises. The derivation chain consists of method description followed by independent empirical evaluation, making the work self-contained against external data.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework rests on the domain assumption that 3D scene models can faithfully encode and propagate semantic, geometric, and physical dependencies under interventions.

axioms (1)
  • domain assumption Editable 3D representations accurately capture and propagate semantic, geometric, and physical dependencies under interventions
    Invoked to justify that rendered outputs remain consistent with scene structure after edits.

pith-pipeline@v0.9.0 · 5539 in / 1108 out tokens · 47845 ms · 2026-05-15T01:30:14.318177+00:00 · methodology

discussion (0)

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