MVG4D: Image Matrix-Based Multi-View and Motion Generation for 4D Content Creation from a Single Image

• URL: http://arxiv.org/abs/2507.18371v2
• Date: Thu, 31 Jul 2025 11:48:37 GMT
• Title: MVG4D: Image Matrix-Based Multi-View and Motion Generation for 4D Content Creation from a Single Image
• Authors: DongFu Yin, Xiaotian Chen, Fei Richard Yu, Xuanchen Li, Xinhao Zhang,
• Abstract summary: We propose MVG4D, a novel framework that generates dynamic 4D content from a single still image.<n>At its core, MVG4D employs an image matrix module that synthesizes temporally coherent and spatially diverse multi-view images.<n>Our method effectively enhances temporal consistency, geometric fidelity, and visual realism, addressing key challenges in motion discontinuity and background degradation.
• Score: 8.22464804794448
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 realism, addressing key challenges in motion discontinuity and background degradation that affect prior 4D GS-based methods. Extensive experiments on the Objaverse dataset demonstrate that MVG4D outperforms state-of-the-art baselines in CLIP-I, PSNR, FVD, and time efficiency. Notably, it reduces flickering artifacts and sharpens structural details across views and time, enabling more immersive AR/VR experiences. MVG4D sets a new direction for efficient and controllable 4D generation from minimal inputs.

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