  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.
