DeePoint: Visual Pointing Recognition and Direction Estimation
This work addresses visual human intention understanding, providing a foundation for applications like human-computer interaction, but it is incremental as it builds on existing gesture recognition methods.
The paper tackles the problem of automatically recognizing and estimating the direction of pointing gestures by introducing the first large-scale dataset (DP Dataset) with over 2 million annotated frames and a novel Transformer-based model (DeePoint) that achieves accurate and efficient results.
In this paper, we realize automatic visual recognition and direction estimation of pointing. We introduce the first neural pointing understanding method based on two key contributions. The first is the introduction of a first-of-its-kind large-scale dataset for pointing recognition and direction estimation, which we refer to as the DP Dataset. DP Dataset consists of more than 2 million frames of 33 people pointing in various styles annotated for each frame with pointing timings and 3D directions. The second is DeePoint, a novel deep network model for joint recognition and 3D direction estimation of pointing. DeePoint is a Transformer-based network which fully leverages the spatio-temporal coordination of the body parts, not just the hands. Through extensive experiments, we demonstrate the accuracy and efficiency of DeePoint. We believe DP Dataset and DeePoint will serve as a sound foundation for visual human intention understanding.