FreeOrbit4D recovers a foreground-complete 4D proxy via decoupled background and object-centric reconstruction to provide geometric guidance for large-angle camera redirection in monocular videos using conditional video diffusion.
PAGE-4D: Disentangled Pose and Geometry Estimation for VGGT-4D Perception
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abstract
Recent 3D feed-forward models, such as the Visual Geometry Grounded Transformer (VGGT), have shown strong capability in inferring 3D attributes of static scenes. However, since they are typically trained on static datasets, these models often struggle in real-world scenarios involving complex dynamic elements, such as moving humans or deformable objects like umbrellas. To address this limitation, we introduce PAGE-4D, a feedforward model that extends VGGT to dynamic scenes, enabling camera pose estimation, depth prediction and point cloud reconstruction - all without post-processing. A central challenge in multitask 4D reconstruction is the inherent conflict between tasks: accurate camera pose estimation requires suppressing dynamic regions, while geometry reconstruction requires modeling them. To resolve this tension, we propose a dynamics aware aggregator that disentangles static and dynamic information by predicting a dynamics-aware mask - suppressing motion cues for pose estimation while amplifying them for geometry reconstruction. Extensive experiments show that PAGE-4D consistently outperforms the original VGGT in dynamic scenarios, achieving superior results in camera pose estimation, monocular and video depth estimation, and dense point map reconstruction. Necessary code and additional demos are available at Link: https://page4d.github.io/. Keywords: VGGT-4D, 4D Perception, Dynamic Scene Reconstruction.
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A training-free progressive decoupling framework improves dynamic depth estimation in 4D reconstruction via mask-guided pose decoupling, topological subspace surgery, and Bayesian fusion, yielding better point-cloud metrics on benchmarks.
GEM-4D is a video world model that injects 4D correspondence supervision to improve geometric consistency and robot manipulation success from 61% to 81%.
GeoWorld-VLM distills geometric structure from camera-conditioned world models into VLMs by aligning visual features, improving spatial reasoning by about 4% on What'sUp and VSR benchmarks across two architectures while preserving language capabilities.
citing papers explorer
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FreeOrbit4D: Training-Free Arbitrary Camera Redirection for Monocular Videos via Foreground-Complete 4D Reconstruction
FreeOrbit4D recovers a foreground-complete 4D proxy via decoupled background and object-centric reconstruction to provide geometric guidance for large-angle camera redirection in monocular videos using conditional video diffusion.
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4DVGGT-D: 4D Visual Geometry Transformer with Improved Dynamic Depth Estimation
A training-free progressive decoupling framework improves dynamic depth estimation in 4D reconstruction via mask-guided pose decoupling, topological subspace surgery, and Bayesian fusion, yielding better point-cloud metrics on benchmarks.
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GEM-4D: Geometry-Enhanced Video World Models for Robot Manipulation
GEM-4D is a video world model that injects 4D correspondence supervision to improve geometric consistency and robot manipulation success from 61% to 81%.
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GeoWorld-VLM: Geometry from World Models for Vision-Language Models
GeoWorld-VLM distills geometric structure from camera-conditioned world models into VLMs by aligning visual features, improving spatial reasoning by about 4% on What'sUp and VSR benchmarks across two architectures while preserving language capabilities.