Recent neural rendering approaches for human activities achieve remarkable view synthesis results, but still rely on dense input views or dense training with all the capture frames, leading to deployment difficulty and inefficient training overload. However, existing advances will be ill-posed if the input is both spatially and temporally sparse. To fill this gap, in this paper we propose a few-shot neural human rendering approach (FNHR) from only sparse RGBD inputs, which exploits the temporal and spatial redundancy to generate photo-realistic free-view output of human activities. Our FNHR is trained only on the key-frames which expand the motion manifold in the input sequences. We introduce a two-branch neural blending to combine the neural point render and classical graphics texturing pipeline, which integrates reliable observations over sparse key-frames. Furthermore, we adopt a patch-based adversarial training process to make use of the local redundancy and avoids over-fitting to the key-frames, which generates fine-detailed rendering results. Extensive experiments demonstrate the effectiveness of our approach to generate high-quality free view-point results for challenging human performances under the sparse setting.

翻译：最近人类活动的神经转换方法取得了引人注目的视觉合成结果,但仍然依赖大量投入观点或密集培训,加上所有捕捉框架,导致部署困难和低效率的培训超负荷;然而,如果输入在空间和时间上都是稀少的,则现有进步将是无法预测的。为了填补这一空白,我们在本文件中建议从稀少的RGBD投入中拿出一个微小的神经合成方法(FNHR),它利用时间和空间冗余来生成人类活动的摄影现实自由视图产出。我们的FNHR仅就扩大输入序列中运动方的关键框架进行了培训。我们引入了两层神经混合,将神经点转化和经典图形纹纹管结合起来,将可靠的观测纳入稀薄的关键框架。此外,我们采用了一个基于补丁基的对抗性培训程序,以利用本地冗余,避免过度适应关键框架,从而产生细化的结果。广泛的实验展示了我们为在草原环境中挑战人类业绩而产生高质量自由视图结果的方法的有效性。