An Attention-Based Signed Distance Field Estimation Method for Hand-Object Reconstruction

Joint reconstruction of hands and objects from monocular RGB images is a challenging task. In this work, we present a novel hybrid model for joint reconstruction of hands and objects. The model proposed consists of three key modules. Among them, multi-scale attention feature extractor is designed to enhance cross-scale in-formation extraction. Attention-based graph encoder can encode the graph information of the hands. Interacting attention module is applied to fuse information between hands and object. Test re-sults on ObMan dataset [6] show that our method outperforms the state-of-the-art method around 13.97% and 15.75% in $\mathrm{H}_{\text{se}}$ and $\mathrm{H}_{\text{je}}$ respectively.