  Synthesizing high-fidelity videos from real-world multi-view input is
challenging because of the complexities of real-world environments and highly
dynamic motions. Previous works based on neural radiance fields have
demonstrated high-quality reconstructions of dynamic scenes. However, training
such models on real-world scenes is time-consuming, usually taking days or
weeks. In this paper, we present a novel method named MixVoxels to better
represent the dynamic scenes with fast training speed and competitive rendering
qualities. The proposed MixVoxels represents the 4D dynamic scenes as a mixture
of static and dynamic voxels and processes them with different networks. In
this way, the computation of the required modalities for static voxels can be
processed by a lightweight model, which essentially reduces the amount of
computation, especially for many daily dynamic scenes dominated by the static
background. To separate the two kinds of voxels, we propose a novel variation
field to estimate the temporal variance of each voxel. For the dynamic voxels,
we design an inner-product time query method to efficiently query multiple time
steps, which is essential to recover the high-dynamic motions. As a result,
with 15 minutes of training for dynamic scenes with inputs of 300-frame videos,
MixVoxels achieves better PSNR than previous methods. Codes and trained models
are available at https://github.com/fengres/mixvoxels
