Novel view synthesis for dynamic scenes is one of the spotlights in computer vision. The key to efficient dynamic view synthesis is to find a compact representation to store the information across time. Though existing methods achieve fast dynamic view synthesis by tensor decomposition or hash grid feature concatenation, their mixed representations ignore the structural difference between time domain and spatial domain, resulting in sub-optimal computation and storage cost. This paper presents T-Code, the efficient decoupled latent code for the time dimension only. The decomposed feature design enables customizing modules to cater for different scenarios with individual specialty and yielding desired results at lower cost. Based on T-Code, we propose our highly compact hybrid neural graphics primitives (HybridNGP) for multi-camera setting and deformation neural graphics primitives with T-Code (DNGP-T) for monocular scenario. Experiments show that HybridNGP delivers high fidelity results at top processing speed with much less storage consumption, while DNGP-T achieves state-of-the-art quality and high training speed for monocular reconstruction.

T-Code是一种高效分解的隐变量编码，可用于时间维度，结合HybridNGP模块实现多摄像头设置下的高保真结果，以及结合DNGP-T模块实现单目场景的最先进质量和高训练速度。

T-Code：简化时态潜在编码以实现高效的动态视图合成