arXiv AI
Lucida: Parse, Generate, and Place for Composable Real-to-Sim Scene Modeling
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arXiv Computer Vision
3d ago
GRASE: Geometry-native Real-to-sim Agentic Scene Modeling from Video
GRASE is a new agentic framework for converting real-world video into editable 3D scenes while preserving geometry and layout. It treats captured multi‑view video as a persistent reference, revising intermediate hypotheses whenever rendered evidence conflicts with the capture. By iteratively refining architectural shells, asset placement, and geometry with cross‑view feedback, GRASE produces faithful, consistent scenes that outperform existing state‑of‑the‑art methods.
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...
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 18
SceneTeract: Probing and Improving Agent-Aware Activity Reasoning in 3D Indoor Scenes
SceneTeract is a verification interface that separates semantic action understanding from physical feasibility in indoor 3D scenes. It decomposes activities into atomic actions and performs explicit geometric checks to determine executability, providing diagnostic traces for failures. The system reveals widespread functional and accessibility issues in synthetic scenes, shows that existing VLMs over‑predict action feasibility, and improves VLM performance through post‑training with verifier feedback, with benefits that generalize to real‑world scenes.
arXiv Computer Vision
Oct 12
Splat2Act: From Captured Scenes to Personalized VLA Manipulation
Splat2Act is an automated real‑to‑sim‑to‑real pipeline that personalizes a pretrained vision‑language‑action policy for a specific indoor scene using only a handheld phone scan and images of the user’s objects. It constructs a photorealistic digital twin with 3D Gaussian splatting, discovers receptacles, generates over 68,000 pickup demonstrations via a simulation oracle, and fine‑tunes the policy in two stages—first on public objects, then on the user’s own items—achieving up to 62.5% pickup success in target scenes. The approach demonstrates that such personalization can be achieved without costly real‑world demonstrations, with a real Stretch 3 robot matching simulated success rates on a 12‑task subset.
arXiv Computer Vision
Sep 7
WorldSculpt: Generating Compositional Worlds from Grounded Videos
WorldSculpt presents a method for generating compositional 3D representations of cluttered scenes with hundreds of objects by adapting a single-object 3D generative prior to multi-view observations. The approach, built on Pixal3D with a multi-view conditioning pathway, can generalize to highly occluded scenes without scene-level training. The authors also introduce the UE-MeshyScene benchmark and demonstrate that their method outperforms prior approaches across various evaluation settings, including converting existing 3DGS worlds into compositional mesh scenes.