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...
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...
By Yumeng He, Yichen Song, Xiaotian Yang, Weijia Zhang, Zanwei Zhou, Junru Gong, Xiaokang Yang, Yunbo Wang
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.
By Tianling Xu, Weijie Wan, Honghao Su, Kailun Su, Junyuan Tang, Huichi Zhou, Haozheng Zhang, Qi Xiong, Ping Luo, Wenbo Ding, Tianxing Chen, Yue Chen
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.
By Tianwei Ye, Yifan Mao, Minwen Liao, Jian Liu, Chunchao Guo, Dazhao Du, Quanxin Shou, Fangqi Zhu, Song Guo
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.
By Yipeng Gao, Lei Shu, Genzhi Ye, Xi Xiong, Ameesh Makadia, Meiqi Guo, Laurent Itti, Jindong Chen
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...
By Jinzhou Tang, Zijun Zhang, Jing Yang, Yuchen Yan, Kun Zhou, Lingjun Mao, Ruobing Han, Jinglin Cao, Wenpeng Xu, Lukun He, Minghao Fu, Fan Feng, Biwei Huang
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...
By Rundong Luo, Yunong Liu, Di Cao, Matthew Tancik, Shyamal Buch, Colton Stearns, Wenqi Xian
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.
By Tianyi Wang, Jiazhou Chen, Yiming Xu, Xiangyu Li, Tianyi Zeng, Chih-Hsien Chou, Ning Lu, Liang Peng, Junfeng Jiao, Christian Claudel
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:2607. 19190v1 Announce Type: cross Abstract: 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.
By Guanxiong Chen, Qianjun Xia, Jiawei Peng, Heng Zhang, Bole Ma, Justin Qian, Ziyi Jiao, Bingyang Zhou, Luoxin Ye, Kaifeng Zhang, Kunyi Wang, Weijia Zeng, Yunuo Chen, Pengzhi Yang, Ziqiu Zeng, Huamin Wang, Chao Liu, Alan Yuille, Fan Shi, Changxi Zheng, Yunzhu Li, Chenfanfu Jiang, Peter Yichen Chen
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.