arXiv Computer Vision
FuncRoom-Agent: Sequential Feed-Forward 3D Functional Indoor Scene Generation
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arXiv Computer Vision
4d ago
SceneReVis: A Self-Reflective Vision-Grounded Framework for 3D Indoor Scene Synthesis via Multi-Turn RL
SceneReVis is a vision‑grounded, self‑reflective framework for 3D indoor scene synthesis that uses an iterative diagnose‑and‑act loop to detect and fix spatial conflicts such as collisions. It introduces SceneChain‑12k, a large dataset of causal construction trajectories generated via a reverse engineering pipeline, and employs a two‑stage training recipe that moves from supervised fine‑tuning to agentic reinforcement learning, turning the model into an active spatial planner. Experiments show that SceneReVis achieves state‑of‑the‑art performance in high‑fidelity generation and goal‑oriented optimization, with better transfer to underrepresented room categories and potential for out‑of‑distribution room types.
arXiv AI
Jun 6
HomeWorld: A Unified Floorplan-to-Furnished Framework for Generating Controllable, Densely Interactive Whole-Home Scenes
arXiv:2606. 06390v1 Announce Type: cross Abstract: Indoor scene generation is crucial for robot simulation and modern interior design.
arXiv AI
Aug 11
Learning an Interior Layout Policy in a Domain Specific Language Action Space
arXiv:2608. 07547v1 Announce Type: cross Abstract: Indoor scene layout generation is a challenging task in interior design.
arXiv Computation and Language
Sep 1
PlanCraft: Sketch, Refine, and Furnish for Architect-Inspired Progressive 3D Residential Scene Generation
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.
Hugging Face Trending Papers
Jul 15
ThinkBLOX: 3D Indoor Scene Generation with Progressive Reasoning
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.
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.