GRASE is a new agentic framework for converting real-world video into editable 3D scenes while preserving geometry and layout. It treats captured multi‑view video as a persistent reference, revising intermediate hypotheses whenever rendered evidence conflicts with the capture. By iteratively refining architectural shells, asset placement, and geometry with cross‑view feedback, GRASE produces faithful, consistent scenes that outperform existing state‑of‑the‑art methods.
By Zhaorui Lin, Haohan Liu, Zhen Chen, Xiangru Huang
arXiv:2609.26795v1 Announce Type: cross
Abstract: 3D Gaussian Splatting (3DGS) can reconstruct a captured scene photorealistically, but the resulting representation does not by itself support physica...
By Runyi Yang, Deheng Zhang, Xiaoye Wang, Kanzhi Wu, Lei Sun, Ajad Chhatkuli, Kunyu Peng, Luc Van Gool, Danda Pani Paudel
3D Gaussian Splatting (3DGS) can reconstruct a captured scene photorealistically, but the resulting representation does not by itself support physical interaction. Robot simulation instead requires ob...
SceneTeract is a verification interface that separates semantic action understanding from physical feasibility in indoor 3D scenes. It decomposes activities into atomic actions and performs explicit geometric checks to determine executability, providing diagnostic traces for failures. The system reveals widespread functional and accessibility issues in synthetic scenes, shows that existing VLMs over‑predict action feasibility, and improves VLM performance through post‑training with verifier feedback, with benefits that generalize to real‑world scenes.
By L\'eopold Maillard, Francis Engelmann, Tom Durand, Boxiao Pan, Yang You, Leonidas Guibas, Maks Ovsjanikov
Splat2Act is an automated real‑to‑sim‑to‑real pipeline that personalizes a pretrained vision‑language‑action policy for a specific indoor scene using only a handheld phone scan and images of the user’s objects. It constructs a photorealistic digital twin with 3D Gaussian splatting, discovers receptacles, generates over 68,000 pickup demonstrations via a simulation oracle, and fine‑tunes the policy in two stages—first on public objects, then on the user’s own items—achieving up to 62.5% pickup success in target scenes. The approach demonstrates that such personalization can be achieved without costly real‑world demonstrations, with a real Stretch 3 robot matching simulated success rates on a 12‑task subset.
By Xiaomeng Ye, Krishna Jaganathan, Gyubeum Lim, Gunjan Chhablani, Muhammad Zubair Irshad, Zsolt Kira
WorldSculpt presents a method for generating compositional 3D representations of cluttered scenes with hundreds of objects by adapting a single-object 3D generative prior to multi-view observations. The approach, built on Pixal3D with a multi-view conditioning pathway, can generalize to highly occluded scenes without scene-level training. The authors also introduce the UE-MeshyScene benchmark and demonstrate that their method outperforms prior approaches across various evaluation settings, including converting existing 3DGS worlds into compositional mesh scenes.
By Muyao Niu, Jixuan He, Ruihan Yu, Lian Fu, Yonghao Yu, Zheng-Hui Huang, Yifan Zhan, Fengbo Lan, Yongtao Ge, Yinqiang Zheng, Kaipeng Zhang, Zhixiang Wang
arXiv:2610.08684v1 Announce Type: new
Abstract: Modern video models can generate realistic videos from real appearance references and proxy renders that specify scene structure, viewpoint changes, an...
By Dicong Qiu, Zhiyuan Xu, Yaosheng Liu, Feng Han, Bo Ye
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
arXiv:2610.16146v1 Announce Type: cross
Abstract: We present Unravel, a training-free framework that turns a single image into an interactive 3D scene, where every object is an independent and movabl...
By Hui Shan, Keyang Luo, Binjia Zhou, Ming Li, Kai Li, Xin Wang, Zhenyang Li, Zesong Yang, Weikai Chen, Runze Zhang, Xin Wang, Yanwei Fu, Zhen Chen, Xiangru Huang
arXiv:2610.10479v1 Announce Type: cross
Abstract: A simulation of a real robot workspace must preserve task-relevant interactions, while policies developed in it must operate on observations availabl...
By Yihan Li, Yating Feng, Shengjiu Sun, Jianing Chen, Hao Ren, Bowen Yang, Weisheng Xu, Qiwei Wu, Hui Cheng, Renjing Xu
arXiv:2603.18532v3 Announce Type: replace-cross
Abstract: The strong performance of large vision-language models (VLMs) trained with reinforcement learning (RL) has motivated similar approaches for f...
By Andrew Choi, Xinjie Wang, Zhizhong Su, Wei Xu
arXiv:2609.36380v1 Announce Type: new
Abstract: A 3D scene reconstructed from a single image is most useful when represented not as a rendering or a fixed 3D output, but as an explicit scene program...
By Xirui Li, Peng Shi, Mingwen Dong, Sheng Zhang, Zhuoyan Xu, Dongkyu Lee, Shuaichen Chang, Yi Xiang, Lin Pan, Jiarong Jiang