Modeling an animatable human avatar in sparse view inputs is highly challenging, especially when synthesizing novel pose images different from the input views. Previous methods suffered from significant image blurring and a lack of clothing wrinkle details due to the spatial transformation process, along with rendering artifacts in the human body caused by self-occlusion issues. To address these issues, we introduce an efficient generalizable geometry-aware human radiance field method for synthesizing high-fidelity novel views and poses from sparse view inputs. To solve the inaccurate feature correspondence caused by human spatial transformation, we propose a human body geometric embedding derived from centroid mapping to provide accurate geometric prior information for guiding the neural radiance field’s learning. Furthermore, we use a geometry-aware attention mechanism consisting of two feature attention modules to address the issue of self-occlusion in sparse view inputs, resulting in improved body shape details and reduced blurriness. Qualitative and quantitative results on the ZJU-MoCap and Thuman datasets demonstrate that our method outperforms state-of-the-art approaches significantly in novel view and pose synthesis tasks.