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Synthesizing high-fidelity videos from real-world multi-view input ischallenging because of the complexities of real-world environments and highlydynamic motions. Previous works based on neural radiance fields havedemonstrated high-quality reconstructions of dynamic scenes. However, trainingsuch models on real-world scenes is time-consuming, usually taking days orweeks. In this paper, we present a novel method named MixVoxels to betterrepresent the dynamic scenes with fast training speed and competitive renderingqualities. The proposed MixVoxels represents the 4D dynamic scenes as a mixtureof static and dynamic voxels and processes them with different networks. Inthis way, the computation of the required modalities for static voxels can beprocessed by a lightweight model, which essentially reduces the amount ofcomputation, especially for many daily dynamic scenes dominated by the staticbackground. To separate the two kinds of voxels, we propose a novel variationfield to estimate the temporal variance of each voxel. For the dynamic voxels,we design an inner-product time query method to efficiently query multiple timesteps, 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 modelsare available at https://github.com/fengres/mixvoxels

Mixed Neural Voxels for Fast Multi-view Video Synthesis