  Reconstructing both objects and hands in 3D from a single RGB image is
complex. Existing methods rely on manually defined hand-object constraints in
Euclidean space, leading to suboptimal feature learning. Compared with
Euclidean space, hyperbolic space better preserves the geometric properties of
meshes thanks to its exponentially-growing space distance, which amplifies the
differences between the features based on similarity. In this work, we propose
the first precise hand-object reconstruction method in hyperbolic space, namely
Dynamic Hyperbolic Attention Network (DHANet), which leverages intrinsic
properties of hyperbolic space to learn representative features. Our method
that projects mesh and image features into a unified hyperbolic space includes
two modules, ie. dynamic hyperbolic graph convolution and image-attention
hyperbolic graph convolution. With these two modules, our method learns mesh
features with rich geometry-image multi-modal information and models better
hand-object interaction. Our method provides a promising alternative for fine
hand-object reconstruction in hyperbolic space. Extensive experiments on three
public datasets demonstrate that our method outperforms most state-of-the-art
methods.
