ScIntBench: Physical Interaction Plausibility in 3D Scene Generation

Generating physically consistent 3D tabletop scenes is a fundamental yet underexplored problem for interactive and generalist robotic learning. The challenge stems from dense object hierarchies and irregular affordances. Here, an interactive scene denotes a physically valid, collision-free environment directly loadable into physics simulators. Existing methods, ranging from decoupled symbolic solvers to end-to-end regression models, often suffer from error propagation or overfitting to noisy supervision containing widespread physical violations. To address these limitations, we introduce PhyScene3D, a framework that reformulates generation as a Human-Mimetic Constructive Process. The proposed Cognitive Topological Reasoning Chain (CTRC) factorizes scene synthesis into a sequential, anchor-conditioned process. It employs a 3D AABB-based placement scheme that imposes a strong structural inductive bias. To address imperfect supervision and physical infeasibility, we introduce Physics-Aware Denoising Alignment (PADA). It integrates a differentiable Signed Distance Field (SDF) with Test-Time Optimization (TTO) to project generated scenes onto a physics-feasible manifold while preserving semantic intent. Experiments demonstrate that PhyScene3D outperforms state-of-the-art approaches in both semantic accuracy and physical validity, achieving a 40% reduction in scene-wise collision rate relative to the human-annotated training data.

Existing methods for single-image 3D indoor scene generation often produce visually plausible results that violate real-world physical constraints, limiting their reliability in robotics, embodied AI, and virtual environment design. To bridge this gap, we introduce a Physics Evaluator that assesses four aspects (geometric priors, contact, stability, and deployability) through nine sub-constraints, establishing a benchmark for evaluating physical consistency. Our analysis reveals that even state-of-the-art methods produce scenes with substantial physical inconsistencies despite their visual plausibility. To address these limitations, we propose a framework that incorporates feedback from the Physics Evaluator into both training and inference to improve the physical plausibility of generated scenes. Specifically, PhyMix comprises two components: (i) implicit alignment via Scene-GRPO, a critic-free group-relative preference optimization strategy that uses physical evaluations to guide the generator toward physically plausible layouts, and (ii) explicit refinement via verifier-guided agentic repair, which translates evaluator diagnostics into executable scene-editing skills to correct residual violations at inference time. PhyMix unifies physical evaluation, preference-based alignment, and agentic inference-time correction to improve physical plausibility while retaining the observed scene content. Experiments show improvements in reconstruction quality and physical plausibility, while qualitative examples on stylized and real-world images illustrate the applicability of the framework beyond synthetic inputs.

Recent progress in image and 3D scene generation has enabled increasingly realistic static environments, yet most methods remain confined to such static configurations. Generating dynamic scenes from natural language is fundamentally challenging: it requires joint reasoning over scene structure, temporal evolution, and physical feasibility, while ensuring reliable execution in modern physics engines. We present a unified framework for generating physically consistent dynamic 3D scenes from text, with outputs directly executable in Unreal Engine. Central to our approach is the Evolutive Scene Graph (ESG), which specifies entities with physical attributes, spatial relations, and event-driven timelines in a machine-checkable form. Given a prompt, a large language model constructs and validates a complete ESG; spatial layouts are grounded via energy-minimized gradient optimization; timeline-constrained physical parameters are then optimized through differentiable simulation to satisfy user-specified events; and the resulting scene is compiled into an engine-executable class. Experiments on 10 scenes across three complexity levels show that our method achieves $16.4/18$ mean event completion, outperforming Scene Language, the strongest engine-executable baseline (SimWorld), and our ablation without physical optimization by a clear margin in event completion and parameter accuracy.