HumanNeRF-SE: A Simple yet Effective Approach to Animate HumanNeRF with Diverse Poses

We present HumanNeRF-SE, which can synthesize diverse novel pose images with simple input. Previous HumanNeRF studies require large neural networks to fit the human appearance and prior knowledge. Subsequent methods build upon this approach with some improvements. Instead, we reconstruct this approach, combining explicit and implicit human representations with both general and specific mapping processes. Our key insight is that explicit shape can filter the information used to fit implicit representation, and frozen general mapping combined with point-specific mapping can effectively avoid overfitting and improve pose generalization performance. Our explicit and implicit human represent combination architecture is extremely effective. This is reflected in our model's ability to synthesize images under arbitrary poses with few-shot input and increase the speed of synthesizing images by 15 times through a reduction in computational complexity without using any existing acceleration modules. Compared to the state-of-the-art HumanNeRF studies, HumanNeRF-SE achieves better performance with fewer learnable parameters and less training time (see Figure 1).