  Nuanced expressiveness, particularly through fine-grained hand and facial
expressions, is pivotal for enhancing the realism and vitality of digital human
representations. In this work, we focus on investigating the expressiveness of
human avatars when learned from monocular RGB video; a setting that introduces
new challenges in capturing and animating fine-grained details. To this end, we
introduce EVA, a drivable human model that meticulously sculpts fine details
based on 3D Gaussians and SMPL-X, an expressive parametric human model. Focused
on enhancing expressiveness, our work makes three key contributions. First, we
highlight the critical importance of aligning the SMPL-X model with RGB frames
for effective avatar learning. Recognizing the limitations of current SMPL-X
prediction methods for in-the-wild videos, we introduce a plug-and-play module
that significantly ameliorates misalignment issues. Second, we propose a
context-aware adaptive density control strategy, which is adaptively adjusting
the gradient thresholds to accommodate the varied granularity across body
parts. Last but not least, we develop a feedback mechanism that predicts
per-pixel confidence to better guide the learning of 3D Gaussians. Extensive
experiments on two benchmarks demonstrate the superiority of our framework both
quantitatively and qualitatively, especially on the fine-grained hand and
facial details. See the project website at \url{https://evahuman.github.io}
