arXiv Computer Vision
Reconstructing the Dynamic World: A Representation-Centric View of 4D Scene Reconstruction
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World from Motion: Generative Dynamic Gaussian Reconstruction from Monocular Video
arXiv:2607. 01202v1 Announce Type: cross Abstract: We present World from Motion, a method for generating freely renderable dynamic 3D Gaussian representations from monocular videos.
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EVolSplat4D: Efficient Volume-based Gaussian Splatting for 4D Urban Scene Synthesis
arXiv:2601.15951v2 Announce Type: replace Abstract: Novel view synthesis (NVS) of static and dynamic urban scenes is essential for autonomous driving simulation, yet existing methods often struggle t...
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A Survey of 3D Reconstruction with Event Cameras
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Dream4D: Lifting Camera-Controlled I2V towards Spatiotemporally Consistent 4D Generation
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arXiv Computer Vision
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Depth Anything V4: Dynamic 4D Scene Reconstruction via Riemannian Flow Matching on 4D Gaussian Splatting
arXiv:2608. 18388v2 Announce Type: replace Abstract: We present Depth Anything V4 (DAV4), a framework for dynamic 4D scene reconstruction from monocular video.
arXiv Computer Vision
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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.