arXiv AI
Unravel: Recovering an Interactive 3D Scene from a Single Image
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Sep 1
Lucida: Parse, Generate, and Place for Composable Real-to-Sim Scene Modeling
arXiv:2608.30821v1 Announce Type: cross Abstract: Composable scene modeling aims to recover a real indoor scene as complete, editable object assets arranged as observed, giving robot simulation and e...
arXiv Computer Vision
Sep 23
\phi-RIE: From Photorealistic Reconstruction to Interactive Environments
arXiv:2609.26795v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3DGS) can reconstruct a captured scene photorealistically, but the resulting representation does not by itself support physica...
arXiv AI
Jun 9
SceneConductor: 3D Scene Generation from Single Image with Multi-Agent Orchestration
arXiv:2606. 08402v1 Announce Type: cross Abstract: Generating complete 3D scenes from a single image requires inferring globally consistent geometry, object relationships, and environmental context from inherently ambiguous visual evidence.
arXiv AI
Jun 16
SceneConductor: 3D Scene Generation from a Single Image with Multi-Agent Orchestration
arXiv:2606. 08402v2 Announce Type: replace-cross Abstract: Generating complete 3D scenes from a single image requires inferring globally consistent geometry, object relationships, and environmental context from inherently ambiguous visual evidence.
Hugging Face Trending Papers
Sep 22
φ-RIE: From Photorealistic Reconstruction to Interactive Environments
3D Gaussian Splatting (3DGS) can reconstruct a captured scene photorealistically, but the resulting representation does not by itself support physical interaction. Robot simulation instead requires ob...
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.