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arxiv logo>cs> arXiv:2212.00190
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Computer Science > Computer Vision and Pattern Recognition

arXiv:2212.00190 (cs)
[Submitted on 1 Dec 2022 (v1), last revised 6 Oct 2023 (this version, v2)]

Title:Mixed Neural Voxels for Fast Multi-view Video Synthesis

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Abstract: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 atthis https URL
Comments:ICCV 2023 (Oral)
Subjects:Computer Vision and Pattern Recognition (cs.CV)
Cite as:arXiv:2212.00190 [cs.CV]
 (orarXiv:2212.00190v2 [cs.CV] for this version)
 https://doi.org/10.48550/arXiv.2212.00190
arXiv-issued DOI via DataCite

Submission history

From: Feng Wang [view email]
[v1] Thu, 1 Dec 2022 00:26:45 UTC (37,194 KB)
[v2] Fri, 6 Oct 2023 22:11:43 UTC (11,478 KB)
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