GaussianFlow: Bridging 4D Content Creation with Gaussian Dynamics
核心概念
GaussianFlow introduces a novel concept, connecting 3D Gaussian dynamics with pixel velocities for efficient 4D content creation.
要約
Introduction: Discusses the challenges in creating 4D fields of Gaussian Splatting from images or videos.
Methodology: Introduces Gaussian flow to connect 3D Gaussians with pixel velocities, enabling direct dynamic supervision.
Experiments: Evaluates the method on 4D generation and novel view synthesis datasets, showcasing state-of-the-art results.
Ablation Study: Validates the effectiveness of flow supervision through qualitative comparisons and visualizations.
Conclusion: Highlights the benefits of GaussianFlow in enhancing Gaussian-based representations for 4D content creation.
GaussianFlow
統計
"Quantitative and qualitative evaluations show that our method achieves state-of-the-art results on both tasks of 4D generation and 4D novel view synthesis."
"Our method drastically improves over existing methods, especially on scene sequences of fast motions."
"Our method also shows less color drifting compared with dynamic NeRF-based method Consistent4D."