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We present Tensor4D, an efficient yet effective approach to dynamic scenemodeling. The key of our solution is an efficient 4D tensor decompositionmethod so that the dynamic scene can be directly represented as a 4Dspatio-temporal tensor. To tackle the accompanying memory issue, we decomposethe 4D tensor hierarchically by projecting it first into three time-awarevolumes and then nine compact feature planes. In this way, spatial informationover time can be simultaneously captured in a compact and memory-efficientmanner. When applying Tensor4D for dynamic scene reconstruction and rendering,we further factorize the 4D fields to different scales in the sense thatstructural motions and dynamic detailed changes can be learned from coarse tofine. The effectiveness of our method is validated on both synthetic andreal-world scenes. Extensive experiments show that our method is able toachieve high-quality dynamic reconstruction and rendering from sparse-viewcamera rigs or even a monocular camera. The code and dataset will be releasedat https://liuyebin.com/tensor4d/tensor4d.html.

Tensor4D : Efficient Neural 4D Decomposition for High-fidelity Dynamic Reconstruction and Rendering