HAVE-FUN: Human Avatar Reconstruction from Few-Shot Unconstrained Images
Authors: Xihe Yang, Xingyu Chen, Daiheng Gao, Shaohui Wang, Xiaoguang Han, Baoyuan Wang

As for human avatar reconstruction contemporary techniques commonly necessitate the acquisition of costly data and struggle to achieve satisfactory results from a small number of casual images. In this paper we investigate this task from a few-shot unconstrained photo album. The reconstruction of human avatars from such data sources is challenging because of limited data amount and dynamic articulated poses. For handling dynamic data we integrate a skinning mechanism with deep marching tetrahedra (DMTet) to form a drivable tetrahedral representation which drives arbitrary mesh topologies generated by the DMTet for the adaptation of unconstrained images. To effectively mine instructive information from few-shot data we devise a two-phase optimization method with few-shot reference and few-shot guidance. The former focuses on aligning avatar identity with reference images while the latter aims to generate plausible appearances for unseen regions. Overall our framework called HaveFun can undertake avatar reconstruction rendering and animation. Extensive experiments on our developed benchmarks demonstrate that HaveFun exhibits substantially superior performance in reconstructing the human body and hand.