SceneForge: Structured World Supervision from 3D Interventions

2025-05-06 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

SceneForge is an intervention-driven framework that generates structured supervision for multimodal learning tasks from editable 3D world states. It represents each scene as a persistent world with semantic, geometric, and physical dependencies, allowing explicit interventions like object removal or camera variation to propagate consistently through scene dependencies. This approach produces aligned outputs such as counterfactual observations, multi-view observations, and effect-aware signals (e.g., shadows, reflections) from a shared world state, avoiding post hoc image-space processing. The framework is instantiated using Infinigen and Blender, creating a licensing-clean indoor supervision resource with over 2,000 scenes, including diverse single-view and registered multi-view settings, and a large number of counterfactual pairs and aligned annotations. Empirical evaluation demonstrates that incorporating SceneForge supervision significantly improves object removal and scene removal performance across multiple benchmarks, even with smaller training budgets, outperforming larger datasets composed of mixed public supervision.

Key takeaway

For research scientists developing multimodal systems that require consistent supervision across edits and viewpoints, SceneForge offers a robust framework. You should consider integrating SceneForge-generated datasets, even with fewer samples, as they provide superior consistency and lead to stronger downstream model performance compared to larger, less structured public datasets. This approach can significantly enhance the fidelity of your training data for tasks like object and scene removal.

Key insights

SceneForge generates consistent multimodal supervision by modeling 3D scene edits as structured world-state transitions.

Principles

• Supervision should derive from editable world states, not observation-level heuristics.
• Interventions propagate effects through semantic, geometric, and physical dependencies.
• Alignment by construction ensures consistency across modalities and views.

Method

SceneForge converts 3D scenes into persistent world states, applies explicit interventions, propagates changes through dependencies, and renders aligned multimodal supervision (e.g., RGB, masks, reflections) for dataset assembly.

In practice

• Use SceneForge to generate counterfactual image pairs for object removal.
• Leverage multi-view consistency for training models in diverse perspectives.
• Decompose scenes into object-linked layers for fine-grained control.

Topics

• SceneForge Framework
• Structured World Supervision
• 3D Interventions
• Multimodal Learning
• Object and Scene Removal

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.