arXiv:2610.28585v1 Announce Type: cross Abstract: Simulation is a powerful tool for robotics, but building diverse, simulation-ready scenes at scale remains difficult. Reconstructing scenes from sing...
WorldMirror is a real‑to‑sim framework that builds interactive digital twins from real‑world robot demonstrations by iteratively refining an executable world program. A coding agent decomposes the observed scene, plans reconstruction steps, and coordinates image and 3D generation tools, using verification feedback to correct errors. The resulting camera‑pose‑anchored scene representation preserves spatial relationships and enables physically consistent interaction and trajectory replay, as shown on the DROID dataset.
SceneAgent is an agentic pipeline that transforms 3D and 4D visual captures—such as 3D Gaussian Splatting, photogrammetry, LiDAR, AI‑generated scenes, and dynamic 4D videos—into interactive simulation environments. It coordinates object reconstruction, physical modeling, part decomposition, articulation, and deformable simulation, and then generates diverse object variants and scripted demonstrations to support policy learning. In benchmark tests, SceneAgent outperforms SimFoundry on cluttered bin scenes and dramatically improves dynamic IoU on 4DCodeBench, while also enabling simulated interactions and preliminary real‑robot policy transfer.
Simulation-ready world reconstruction demands physical interactivity beyond visual realism, which existing black-box 3D generation fails to deliver. We present NeoWorld-3, a coding-agent framework tha...
The advancement of Embodied AI necessitates high-quality simulation assets that faithfully mirror the real world. However, transforming raw visual observations into simulation-ready scenes remains cha...
arXiv:2610.27690v1 Announce Type: new Abstract: Simulation-ready world reconstruction demands physical interactivity beyond visual realism, which existing black-box 3D generation fails to deliver. We...
arXiv:2608.24212v1 Announce Type: new Abstract: The advancement of Embodied AI necessitates high-quality simulation assets that faithfully mirror the real world. However, transforming raw visual obse...
PhyMix introduces a Physics Evaluator that checks geometric priors, contact, stability, and deployability through nine sub-constraints, establishing a benchmark for physical consistency in single-image 3D indoor scene generation. The framework uses implicit alignment via Scene-GRPO and explicit refinement through verifier-guided agentic repair to incorporate evaluator feedback during training and inference, improving physical plausibility while preserving scene content. Experiments demonstrate enhanced reconstruction quality and physical plausibility, with qualitative results on stylized and real-world images showing the method’s broader applicability.
arXiv:2609.36024v1 Announce Type: new Abstract: Reconstructing simulation-ready 3D scenes from real-world observations enables robotics, gaming, and immersive applications, yet existing methods large...
arXiv:2608.30821v1 Announce Type: cross Abstract: Composable scene modeling aims to recover a real indoor scene as complete, editable object assets arranged as observed, giving robot simulation and e...
arXiv:2609.23103v1 Announce Type: cross Abstract: While simulation-ready deformable assets are essential for in-silico robotic manipulation tasks, existing generation frameworks typically assess phys...
LLM agents are increasingly used to translate natural language into 3D scenes in a procedural way, but existing systems focus on static output. Dynamic 4D scenes from text alone, in which liquids flow, particles emit, rigid bodies cascade, and articulated mechanisms move, remain largely unexplored despite their value as editable content and as physics-grounded training data for video generation and embodied AI.