Reproduce “GaussianFlow: Splatting Gaussian Dynamics for 4D Content Creation” on OpenResearch
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About this paper
Quankai Gao, Qiangeng Xu, Zhe Cao, Ben Mildenhall, Wenchao Ma, Le Chen, Danhang Tang, Ulrich Neumann
GaussianFlow introduces a novel concept, 'Gaussian flow,' to analytically and differentiably connect 3D Gaussian dynamics to 2D pixel velocities. This enables direct optical flow supervision for 4D Gaussian Splatting, achieving state-of-the-art dynamic content generation and novel view synthesis with enhanced temporal consistency and motion realism.
Key insights
- The relative position of a pixel within a deforming 2D Gaussian's coordinate system can be assumed constant between time steps, allowing for analytical derivation of its flow.
- Similar to pixel color, a pixel's total velocity can be composed as a weighted sum of individual 3D Gaussian flow contributions, using standard alpha-blending weights.
- Directly supervising 3D Gaussian dynamics with 2D optical flow helps resolve the motion-appearance ambiguity in 4D Gaussian Splatting, ensuring more realistic underlying motions from all viewpoints.
Summary by alphaXiv.
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