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.
arXiv:2608.21438v1 Announce Type: new Abstract: Text guided 3D scene editing provides an intuitive interface for modifying reconstructed environments, but remains difficult because natural language d...
The paper introduces GREP, a framework for evaluating AI-generated furnished rooms that moves beyond specific questions to a three-tier philosophy: basic physical validity, visual coherence, and human-use logic. An autonomous evaluation agent applies these principles to identify scene issues, produce scores, and generate reports, enabling reliable, scene-specific judgments. Experiments demonstrate GREP’s high agreement with human preferences, its ability to improve scene quality through feedback, and insights into how future models might better balance planning and constraint adaptation.
arXiv:2608. 06161v1 Announce Type: new Abstract: Synthetic 3D scene generation is increasingly used as a data source for computer vision and embodied AI, but existing generators often optimize perceptual realism without reliably satisfying task-critical functional constraints.
The paper introduces a unified training framework for automatic PowerPoint generation, combining cross‑validated data synthesis, rule‑based teacher enhancement, and a mixed‑teacher supervised fine‑tuning strategy. It further adds a reinforcement learning component with verifiable aesthetic metrics to refine slide visual attributes. Experiments show significant performance gains on PresentBench, outperforming existing models and improving aesthetic quality.
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: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...
arXiv:2607. 00407v1 Announce Type: new Abstract: Slide design requires personalizing both deck themes and page layouts.
arXiv:2607. 01766v1 Announce Type: new Abstract: LLM agents are increasingly used to translate natural language into 3D scenes in a procedural way, but existing systems focus on static output.
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.
arXiv:2508. 09860v2 Announce Type: replace Abstract: Human-aligned AI is a critical component of co-creativity, as it enables models to accurately interpret human intent and generate controllable outputs that align with design goals in collaborative content creation.
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.
LLM agents are increasingly used to translate natural language into 3D scenes in a procedural way, but existing systems focus on static output. Dynamic 4D scenes from text alone, in which liquids flow, particles emit, rigid bodies cascade, and articulated mechanisms move, remain largely unexplored despite their value as editable content and as physics-grounded training data for video generation and embodied AI.