arXiv Computer Vision

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 3

RoGe: Novel View Synthesis via End-to-End Implicit Reconstruction and Generation

RoGe is a new end‑to‑end framework for novel view synthesis that jointly learns an implicit 3D scene representation and a video diffusion model. It eliminates the need for explicit 3D intermediates by querying the implicit scene with camera rays to produce geometric features that condition the diffusion model. Experiments on DL3DV show that RoGe surpasses reconstruction‑based, generation‑based, and hybrid baselines in image quality and temporal consistency, and ablations confirm the benefits of ray‑queried features and joint training.

By Xiaolei Lang, Ze Kang, Zehao Huang, Naiyan Wang
arXiv Computer Vision
Aug 24

Grounded-Exo2Ego: Structured Semantic Grounding for Robust Exocentric-to-Egocentric Video Generation

Grounded-Exo2Ego introduces a dual‑branch video diffusion model that combines a geometric anchoring branch with a semantic grounding branch to generate egocentric video from a single exocentric source. The framework also includes a camera re‑localization algorithm to correct reconstruction misalignment and a fully automated synthetic data engine for training. Experiments on the EgoExo4D dataset demonstrate significant performance gains over recent state‑of‑the‑art methods.

By Shengze Wang, Michael Stengel, Tianye Li, Seonwook Park, Amrita Mazumdar, Koki Nagano, Alex Trevithick, Shalini De Mello
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.

By Muyao Niu, Jixuan He, Ruihan Yu, Lian Fu, Yonghao Yu, Zheng-Hui Huang, Yifan Zhan, Fengbo Lan, Yongtao Ge, Yinqiang Zheng, Kaipeng Zhang, Zhixiang Wang
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.

By Shufan Sun, Chen Wang, Enxin Song, Jiatao Gu, Lingjie Liu