  Reconstructing 3D clothed human avatars from single images is a challenging
task, especially when encountering complex poses and loose clothing. Current
methods exhibit limitations in performance, largely attributable to their
dependence on insufficient 2D image features and inconsistent query methods.
Owing to this, we present the Global-correlated 3D-decoupling Transformer for
clothed Avatar reconstruction (GTA), a novel transformer-based architecture
that reconstructs clothed human avatars from monocular images. Our approach
leverages transformer architectures by utilizing a Vision Transformer model as
an encoder for capturing global-correlated image features. Subsequently, our
innovative 3D-decoupling decoder employs cross-attention to decouple tri-plane
features, using learnable embeddings as queries for cross-plane generation. To
effectively enhance feature fusion with the tri-plane 3D feature and human body
prior, we propose a hybrid prior fusion strategy combining spatial and
prior-enhanced queries, leveraging the benefits of spatial localization and
human body prior knowledge. Comprehensive experiments on CAPE and THuman2.0
datasets illustrate that our method outperforms state-of-the-art approaches in
both geometry and texture reconstruction, exhibiting high robustness to
challenging poses and loose clothing, and producing higher-resolution textures.
Codes will be available at https://github.com/River-Zhang/GTA.
