  We present an approach that can reconstruct hands in 3D from monocular input.
Our approach for Hand Mesh Recovery, HaMeR, follows a fully transformer-based
architecture and can analyze hands with significantly increased accuracy and
robustness compared to previous work. The key to HaMeR's success lies in
scaling up both the data used for training and the capacity of the deep network
for hand reconstruction. For training data, we combine multiple datasets that
contain 2D or 3D hand annotations. For the deep model, we use a large scale
Vision Transformer architecture. Our final model consistently outperforms the
previous baselines on popular 3D hand pose benchmarks. To further evaluate the
effect of our design in non-controlled settings, we annotate existing
in-the-wild datasets with 2D hand keypoint annotations. On this newly collected
dataset of annotations, HInt, we demonstrate significant improvements over
existing baselines. We make our code, data and models available on the project
website: https://geopavlakos.github.io/hamer/.
