Scene-Designer is an agentic framework that learns design intelligence for generating 3D indoor scenes from natural language. It introduces two datasets—Scene-OTA-35K for design and error correction, and Scene-OTC-15K for quality critique—and a Critic-guided GRPO framework (Scene-GRPO) that refines design decisions. Experiments show that Scene-Designer outperforms existing methods in physical validity, functional correctness, and layout coherence, and that its learned Critic provides effective guidance during test time.
By Xuanyu Liu, Hongyan Feng, Miao Pan, Sunlai Chen, Lizhou Liang, Bing Sun, Wenqi Zhang, Xuhong Zhang
arXiv:2610.14397v2 Announce Type: cross
Abstract: Augmenting worker capabilities in the physical world through guidance has been a decades-long goal for AI agents. One key supporting capability is to...
By Andrew Perrault (Relantic, Inc., Columbus, OH, USA), Anmol Takiar (Relantic, Inc., Columbus, OH, USA), Dominik Winecki (Relantic, Inc., Columbus, OH, USA), Arnab Nandi (Relantic, Inc., Columbus, OH, USA)
PlanCraft introduces a progressive approach to 3D residential scene generation that mirrors how architects design: starting with rough sketches and refining them over time. It leverages a large dataset of real floor plans to train a SketchPlan module that generates partial sketches at various completion levels, a PlanCraft‑Diff module that sharpens these sketches into precise vector floor plans, and a PlanCraft‑Agent that furnishes rooms within the established spatial contract. The method outperforms existing 2D and 3D baselines, achieving a 61.1% lower FID and a 15‑point lead in expert‑rated spatial rationality, even with only 25% sketch completion.
By Pengyu Zeng, Yuqin Dai, Jun Yin, Ziyang Han, Ng Cheuk Hei, Jing Zhong, Chaoyang Shi, ZhanXiang Jin, Maowei Jiang, Shuai Lu
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
Code4Scene is a benchmark that evaluates coding agents on constructing and editing 3D scenes in Unreal Engine. It tests agents on two tasks: construction, where they must build a scene from open‑ended language, and editing, where they must recover a target scene from reference images while preserving everything else. The benchmark measures task fulfillment, artifact integrity, and physical validity, revealing that construction and editing performance are correlated but not interchangeable, with agents struggling most with spatial composition and precise edits.
By Xiaokang Ye, Siddhant Hitesh Mantri, Zimeng Chen, Edward Zhang, Zhaoxu Zheng, Yuanheng Li, Yizhao Chen, Tianyang Huang, Lianhui Qin
arXiv:2602. 09153v2 Announce Type: replace-cross Abstract: Simulation has become a key tool for training and evaluating home robots at scale, yet existing environments fail to capture the diversity and physical complexity of real indoor spaces.
By Nicholas Pfaff, Thomas Cohn, Sergey Zakharov, Rick Cory, Russ Tedrake
arXiv:2608.29519v1 Announce Type: new
Abstract: We introduce Function-Room Generation, a new indoor 3D scene generation setting that creates rooms supporting explicit functional goals rather than mer...
By Hao Feng, Zhi Zuo, MingJian Liang, Jingyu Hu, Xiaowei Hu, Liupengfei Wu, Dian Zhang, Guoxin Fang, Zhengzhe Liu
ARGOS is a new benchmark and agent framework for multi‑camera person search that transforms the task from one‑shot retrieval into interactive reasoning using partial witness clues. The framework requires agents to plan questions, use spatial or temporal tools, and interpret ambiguous natural‑language responses within a limited turn budget, leveraging a Spatio‑Temporal Topology Graph that encodes camera connectivity and transition times. The benchmark includes 2,691 tasks across 14 real‑world scenarios, divided into semantic, spatial, and temporal tracks, and introduces Turn‑Weighted Success (TWS) as a metric that jointly measures correctness and turn efficiency, with current best agents achieving TWS scores of 0.383 and 0.590 on the spatial and temporal tracks respectively.
By Myungchul Kim, Kwanyong Park, Junmo Kim, In So Kweon
arXiv:2606. 10953v1 Announce Type: new Abstract: Furnished floor plans are fundamental to real estate visualization, interior design, and architectural workflows.
By Fedor Rodionov, Aleksandar Cvejic, Michael Birsak, John Femiani, Peter Wonka
HARMONY is a hierarchical chain-of-thought framework that reconstructs complete 3D indoor scenes from a single monocular image. It combines agentic reasoning with visual geometry foundation models, starting with camera calibration and semantic layout recovery, then placing objects hierarchically while refining geometry with point cloud estimations. The method achieves semantically consistent scenes that align perceptually with the input image, outperforming existing baselines on synthetic and real-world data.
By Shufan Sun, Chen Wang, Enxin Song, Jiatao Gu, Lingjie Liu
arXiv:2606. 16122v1 Announce Type: new Abstract: Visual thinking should not only sound right; it should show its evidence.
By Junkai Zhang, Yihe Deng, Kai-Wei Chang, Wei Wang
arXiv:2605. 05407v2 Announce Type: replace Abstract: Scaling LLM-based embodied agents from text-only environments to complex multimodal settings remains a major challenge.
By Mohamed Salim Aissi, Clemence Grislain, Clement Romac, Laure Soulier, Mohamed Chetouani, Olivier Sigaud, Nicolas Thome