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Reconstructing dynamic 3D scenes from 2D images and generating diverse viewsover time is challenging due to scene complexity and temporal dynamics. Despiteadvancements in neural implicit models, limitations persist: (i) InadequateScene Structure: Existing methods struggle to reveal the spatial and temporalstructure of dynamic scenes from directly learning the complex 6D plenopticfunction. (ii) Scaling Deformation Modeling: Explicitly modeling scene elementdeformation becomes impractical for complex dynamics. To address these issues,we consider the spacetime as an entirety and propose to approximate theunderlying spatio-temporal 4D volume of a dynamic scene by optimizing acollection of 4D primitives, with explicit geometry and appearance modeling.Learning to optimize the 4D primitives enables us to synthesize novel views atany desired time with our tailored rendering routine. Our model is conceptuallysimple, consisting of a 4D Gaussian parameterized by anisotropic ellipses thatcan rotate arbitrarily in space and time, as well as view-dependent andtime-evolved appearance represented by the coefficient of 4D spherindricalharmonics. This approach offers simplicity, flexibility for variable-lengthvideo and end-to-end training, and efficient real-time rendering, making itsuitable for capturing complex dynamic scene motions. Experiments acrossvarious benchmarks, including monocular and multi-view scenarios, demonstrateour 4DGS model's superior visual quality and efficiency.

Real-time Photorealistic Dynamic Scene Representation and Rendering with 4D Gaussian Splatting