Video4DGen: Enhancing Video and 4D Generation through Mutual Optimization.
🤖 AI Summary
This work addresses the problem of reconstructing high-fidelity, spatiotemporally consistent 4D dynamic scenes from single or multiple videos and enabling 4D-guided novel-view video synthesis. To this end, we propose a joint optimization framework integrating Dynamic Gaussian Splats (DGS) with a differentiable continuous deformation field. We introduce two key innovations: (i) warped-state geometric regularization and confidence-weighted DGS rendering; and (ii) multi-video cross-view alignment, root-pose co-optimization, and pose-guided sampling—collectively enhancing geometric reconstruction accuracy and appearance detail under complex motion. The method preserves strong spatiotemporal consistency while significantly improving novel-view generalization. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods in both 4D reconstruction quality and 4D-guided video generation performance. The framework is applicable to digital human modeling, virtual reality, and animation production.
Application Category
📝 Abstract
The advancement of 4D (i.e., sequential 3D) generation opens up new possibilities for lifelike experiences in various applications, where users can explore dynamic objects or characters from any viewpoint. Meanwhile, video generative models are receiving particular attention given their ability to produce realistic and imaginative frames. These models are also observed to exhibit strong 3D consistency, indicating the potential to act as world simulators. In this work, we present Video4DGen, a novel framework that excels in generating 4D representations from single or multiple generated videos as well as generating 4D-guided videos. This framework is pivotal for creating high-fidelity virtual contents that maintain both spatial and temporal coherence. The 4D outputs generated by Video4DGen are represented using our proposed Dynamic Gaussian Surfels (DGS), which optimizes time-varying warping functions to transform Gaussian surfels (surface elements) from a static state to a dynamically warped state. We design warped-state geometric regularization and refinements on Gaussian surfels, to preserve the structural integrity and fine-grained appearance details, respectively. Additionally, in order to perform 4D generation from multiple videos and effectively capture representation across spatial, temporal, and pose dimensions, we design multi-video alignment, root pose optimization, and pose-guided frame sampling strategies. The leveraging of continuous warping fields also enables a precise depiction of pose, motion, and deformation over per-video frames. Further, to improve the overall fidelity from the observation of all camera poses, Video4DGen performs novel-view video generation guided by the 4D content, with the proposed confidence-filtered DGS to enhance the quality of generated sequences. In summary, Video4DGen yields dynamic 4D generation with the ability to handle different subject movements, while preserving details in both geometry and appearance. The framework also generates 4D-guided videos with high spatial and temporal coherence. With the ability of 4D and video generation, Video4DGen offers a powerful tool for applications in virtual reality, animation, and beyond.
Problem
Research questions and friction points this paper is trying to address.
Enhancing 4D and video generation through mutual optimization
Creating high-fidelity virtual content with spatiotemporal coherence
Improving pose, motion, and deformation depiction in generated sequences
Innovation
Methods, ideas, or system contributions that make the work stand out.
Dynamic Gaussian Surfels optimize time-varying warping functions
Multi-video alignment captures spatial-temporal-pose dimensions
Confidence-filtered DGS enhances novel-view video generation
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