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...
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...
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.
arXiv:2606. 01057v1 Announce Type: cross Abstract: Procedural 3D modeling through code is emerging as a versatile paradigm, offering deterministic, engine-ready, and precisely editable assets that neural 3D generators inherently lack.
The paper surveys 3D generation techniques tailored for embodied AI and robotics, emphasizing that beyond visual realism, generated content must include kinematic structure, material properties, and interaction capabilities. It categorizes the literature into three roles: Data Generator, Simulation Environments, and Sim2Real Bridge, each addressing different needs in training and deployment. The survey highlights a shift toward interaction readiness and identifies key bottlenecks such as limited physical annotations, quality gaps, fragmented evaluation, and the sim‑to‑real divide.
Simulation is a powerful tool for robotics, but building diverse, simulation-ready scenes at scale remains difficult. Reconstructing scenes from single images is a promising alternative, but visual si...
arXiv:2610.04432v2 Announce Type: replace Abstract: Building interactive simulators from real-world observations is a promising way to scale embodied data, but current pipelines still rely heavily on...
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...
LiteReality-Agent is an agentic system that reconstructs real indoor environments into realistic, articulated, and simulation-ready 3D scenes from RGB‑D scans. It treats reconstruction as a coding problem, where a coding agent iteratively edits a Python script (Room.py) using specialized tools, while an observe‑edit‑verify harness ensures evidence gathering, measurement, verification, layout optimisation, simulation readiness, and quality control. The system outperforms recent models like Astra and Fable in geometric accuracy, visual realism, and simulation compatibility, and its modular framework is positioned as a robust foundation for future agent‑driven reconstruction tasks.
RoboPhys-3D is a 3D‑grounded embodied world model benchmark built on RoboTwin 2.0, featuring 50 manipulation tasks, 5,000 episodes, and 25,000 multi‑view ground‑truth videos. It evaluates video world models by processing both generated and ground‑truth videos through the same 3D reconstruction pipeline, allowing the separation of reconstruction‑induced from generation‑induced errors. The benchmark defines 50 metrics across four sub‑dimensions—pixel fidelity, 3D geometry consistency, state understanding, and task completeness—and introduces the Average Full Score and RoboPhyscore for holistic assessment, with RoboPhyscore showing strong correlation with human judgments.
Real-to-sim conversion for robotic interaction with objects remains labor-intensive because it requires more than visual reconstruction: a streamlined real2sim process must recover scene geometries and object states, infer physical parameters, and assemble actors, objects, cameras, poses, and trajectories into a runnable physical simulation. Today this process still depends on manual tuning of visual foundation models, mesh cleanup, coordinate-frame alignment, and brittle workflow glue across visual perception tools and simulators.