Hugging Face Trending Papers

ThinkBLOX: 3D Indoor Scene Generation with Progressive Reasoning

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While traditional graphics methods often synthesize 3D indoor scenes autoregressively or hierarchically, recent vision-language model (VLM)-based generators predominantly adopt a one-shot paradigm where the full layout is planned at once. This one-shot approach often requires global re-optimization or complete reconstruction during interactive editing (e.

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arXiv AI
Sep 1

ScenePilot: Grow-and-Repair Policy for Text-Driven 3D Indoor Scene Generation

ScenePilot introduces a retrieval‑augmented Grow‑and‑Repair framework for text‑driven 3D indoor scene generation. It uses a Hierarchical Retrieval‑Augmented Planning module to fetch room, group, and anchor layout priors, then incrementally inserts object groups with a base generator, while a Reinforcement Multimodal Repair module performs lightweight local corrections after each insertion and a final global repair. The approach is trained on a new SceneReverse‑17k dataset of perturbed scenes, enabling the policy to predict structured move‑rotate‑scale actions from rendered views, scene state, retrieved priors, and edit history, thereby improving physical plausibility, functional coherence, and controllability without heavy full‑scene optimization.

By Jiawei Zhang, Hongsong Wang, Pan Zhou
arXiv Computer Vision
Sep 23

Fysiverse-3D-Vision Technical Report: Generating Executable 3D Worlds from Images through Unified Spatial Reasoning

Fysiverse-3D-Vision is a unified vision‑language‑geometry framework that reconstructs executable 3D scenes from a single image. It separates spatial layout reasoning from asset synthesis, using a shared representation where spatial reasoning and geometric reconstruction reinforce each other. The model employs a Transformer that integrates textual supervision, semantic visual cues, and geometric representations, and includes an object‑conditioned layout module to predict object translation, rotation, and scale while maintaining physical consistency through collision‑aware optimization.

By Dingkang Yang, Yizhou Liu, Wendong Cheng, Zizhi Chen, Shunli Wang, Yang Liu, Hongsheng Li, Lihua Zhang
arXiv AI
Aug 7

CoCo: Code as CoT for Text-to-Image Preview and Rare Concept Generation

arXiv:2603. 08652v2 Announce Type: replace Abstract: Recent advancements in Unified Multimodal Models (UMMs) have significantly advanced text-to-image (T2I) generation, particularly through the integration of Chain-of-Thought (CoT) reasoning.

By Haodong Li, Chunmei Qing, Huanyu Zhang, Dongzhi Jiang, Yihang Zou, Hongbo Peng, Dingming Li, Yuhong Dai, ZePeng Lin, Juanxi Tian, Yi Zhou, Siqi Dai, Jingwei Wu, Pheng-Ann Heng