arXiv AI By Yuhan Liu, Lianhui Qin, Shenji Wan

SceneBloom: Agentic Indoor Environment Generation with Realistic and Diverse Layouts for Embodied Learning

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SceneBloom is an agentic framework that generates realistic, varied, and simulation-ready indoor environments for embodied learning. It combines diffusion-based visual layout sampling with a contract-guided 3D compiler that uses extracted relations for geometric fitting and pose correction. The method outperforms baselines across five room types, achieving higher visual quality, physical stability, and better alignment with real room distributions, and improves object navigation policy success rates on real scans.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Computer Vision
Sep 23

HARMONY: Hierarchical Agentic Reasoning for MONocular Image-to-Scene Synthesis

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
Hugging Face Trending Papers
Aug 19

Beyond Placement and Articulation: Usage-Driven Code Scenes for Embodied Interaction

The paper introduces RoomWright, a code‑driven framework that generates 3D indoor scenes for embodied AI by focusing on functional usage rather than just visual layout. It performs usage‑driven object reasoning, treating anchors as task centers to select task‑required objects and their affordances, and compiles interactions into trigger‑condition‑effect rules that update object states. The system also addresses ambiguous object orientation through annotation‑guided usage cues, producing scenes that are executable, editable, and ready for simulation‑based policy learning.

arXiv AI
Oct 21

GALA: Geometry-Aware Language Model for Controllable Object Arrangement

arXiv:2610.28280v1 Announce Type: cross Abstract: Reasoning about fine-grained placement constraints, such as support and containment, is essential for realistic 3D scene creation. It remains a signi...

By Chucheng Xiang, Runze Wang, Zhi Deng, Ruchao Bao, Yuanwei Zhang, Yuxuan Xie, Hanliu Wang, Liangzhen Fei, Wenzheng Wu, Cheng Wan, Peifeng Li, Zhongyuan Liu, Ligang Liu