  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. Project website: https://seanchenxy.github.io/HaveFunWeb/.
