MVG4D: Image Matrix-Based Multi-View and Motion Generation for 4D Content Creation from a Single Image
DongFu Yin, Xiaotian Chen, Fei Richard Yu, Xuanchen Li, Xinhao Zhang

TL;DR
MVG4D is a novel framework that generates high-quality, temporally consistent 4D content from a single image by combining multi-view synthesis with 4D Gaussian Splatting, improving realism and efficiency.
Contribution
It introduces a new method that synthesizes multi-view images and extends 3D Gaussian point clouds into the temporal domain for 4D content creation from a single image.
Findings
Outperforms state-of-the-art baselines in multiple metrics
Reduces flickering artifacts and sharpens structural details
Enhances temporal consistency and visual realism
Abstract
Advances in generative modeling have significantly enhanced digital content creation, extending from 2D images to complex 3D and 4D scenes. Despite substantial progress, producing high-fidelity and temporally consistent dynamic 4D content remains a challenge. In this paper, we propose MVG4D, a novel framework that generates dynamic 4D content from a single still image by combining multi-view synthesis with 4D Gaussian Splatting (4D GS). At its core, MVG4D employs an image matrix module that synthesizes temporally coherent and spatially diverse multi-view images, providing rich supervisory signals for downstream 3D and 4D reconstruction. These multi-view images are used to optimize a 3D Gaussian point cloud, which is further extended into the temporal domain via a lightweight deformation network. Our method effectively enhances temporal consistency, geometric fidelity, and visual…
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Taxonomy
TopicsAdvanced Vision and Imaging · Augmented Reality Applications · Computer Graphics and Visualization Techniques
