Nuanced expressiveness, especially through detailed hand and facial expressions, is pivotal for enhancing the realism and vitality of digital human representations. In this work, we aim to learn expressive human avatars from a 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 can recover fine details based on 3D Gaussians and an expressive parametric human model, SMPL-X. Focused on enhancing expressiveness, our work makes three key contributions. First, we highlight the importance of aligning the SMPL-X model with the video frames for effective avatar learning. Recognizing the limitations of current methods for estimating SMPL-X parameters from in-the-wild videos, we introduce a reconstruction module that significantly improves the image-model alignment. 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. Third, we develop a feedback mechanism that predicts per-pixel confidence to better guide the optimization of 3D Gaussians. Extensive experiments on two benchmarks demonstrate the superiority of our approach both quantitatively and qualitatively, especially on the fine-grained hand and facial details. We make our code available at the project website: https://evahuman.github.io.

Novel view synthesis has been heavily driven by NeRF-based models, but these models often hold limitations with the requirement of dense coverage of input views and expensive computations. NeRF models designed for scenarios with a few sparse input views face difficulty in being generalizable to complex or unbounded scenes, where multiple scene content can be at any distance from a multi-directional camera, and thus generate unnatural and low quality images with blurry or floating artifacts. To accommodate the lack of dense information in sparse view scenarios and the computational burden of NeRF-based models in novel view synthesis, our approach adopts diffusion models. In this paper, we present PoseDiff, which combines the fast and plausible generation ability of diffusion models and 3D-aware view consistency of pose parameters from NeRF-based models. Specifically, PoseDiff is a multimodal pose-conditioned diffusion model applicable for novel view synthesis of unbounded scenes as well as bounded or forward-facing scenes with sparse views. PoseDiff renders plausible novel views for given pose parameters while maintaining high-frequency geometric details in significantly less time than conventional NeRF-based methods.