  We present a novel pipeline for learning high-quality triangular human
avatars from multi-view videos. Recent methods for avatar learning are
typically based on neural radiance fields (NeRF), which is not compatible with
traditional graphics pipeline and poses great challenges for operations like
editing or synthesizing under different environments. To overcome these
limitations, our method represents the avatar with an explicit triangular mesh
extracted from an implicit SDF field, complemented by an implicit material
field conditioned on given poses. Leveraging this triangular avatar
representation, we incorporate physics-based rendering to accurately decompose
geometry and texture. To enhance both the geometric and appearance details, we
further employ a 2D UNet as the network backbone and introduce pseudo normal
ground-truth as additional supervision. Experiments show that our method can
learn triangular avatars with high-quality geometry reconstruction and
plausible material decomposition, inherently supporting editing, manipulation
or relighting operations.
