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Neural radiance field (NeRF) has achieved great success in novel viewsynthesis and 3D representation for static scenarios. Existing dynamic NeRFsusually exploit a locally dense grid to fit the deformation field; however,they fail to capture the global dynamics and concomitantly yield models ofheavy parameters. We observe that the 4D space is inherently sparse. Firstly,the deformation field is sparse in spatial but dense in temporal due to thecontinuity of of motion. Secondly, the radiance field is only valid on thesurface of the underlying scene, usually occupying a small fraction of thewhole space. We thus propose to represent the 4D scene using a learnable sparselatent space, a.k.a. SLS4D. Specifically, SLS4D first uses dense learnable timeslot features to depict the temporal space, from which the deformation field isfitted with linear multi-layer perceptions (MLP) to predict the displacement ofa 3D position at any time. It then learns the spatial features of a 3D positionusing another sparse latent space. This is achieved by learning the adaptiveweights of each latent code with the attention mechanism. Extensive experimentsdemonstrate the effectiveness of our SLS4D: it achieves the best 4D novel viewsynthesis using only about $6\%$ parameters of the most recent work.

SLS4D: Sparse Latent Space for 4D Novel View Synthesis