  The research fields of parametric face model and 3D face reconstruction have
been extensively studied. However, a critical question remains unanswered: how
to tailor the face model for specific reconstruction settings. We argue that
reconstruction with multi-view uncalibrated images demands a new model with
stronger capacity. Our study shifts attention from data-dependent 3D Morphable
Models (3DMM) to an understudied human-designed skinning model. We propose
Adaptive Skinning Model (ASM), which redefines the skinning model with more
compact and fully tunable parameters. With extensive experiments, we
demonstrate that ASM achieves significantly improved capacity than 3DMM, with
the additional advantage of model size and easy implementation for new
topology. We achieve state-of-the-art performance with ASM for multi-view
reconstruction on the Florence MICC Coop benchmark. Our quantitative analysis
demonstrates the importance of a high-capacity model for fully exploiting
abundant information from multi-view input in reconstruction. Furthermore, our
model with physical-semantic parameters can be directly utilized for real-world
applications, such as in-game avatar creation. As a result, our work opens up
new research direction for parametric face model and facilitates future
research on multi-view reconstruction.
