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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
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 temporaldoi:10.24963/ijcai.2021/130 fatcat:cxxo23s4knc6ln3tkngnc2ys5u