Understanding Egocentric Hand-Object Interactions from Hand Pose Estimation
This work addresses hand-object interaction analysis for applications like robotics or AR/VR, but it appears incremental as it builds on existing datasets and methods.
The paper tackles the problem of estimating hand pose from egocentric views during hand-object interactions by proposing a method to label a pairwise dataset and training an encoder-decoder network, resulting in improved training efficiency and testing accuracy, outperforming state-of-the-art methods on the GANerated dataset and achieving better grasp type classification on the GUN-71 dataset.
In this paper, we address the problem of estimating the hand pose from the egocentric view when the hand is interacting with objects. Specifically, we propose a method to label a dataset Ego-Siam which contains the egocentric images pair-wisely. We also use the collected pairwise data to train our encoder-decoder style network which has been proven efficient in. This could bring extra training efficiency and testing accuracy. Our network is lightweight and can be performed with over 30 FPS with an outdated GPU. We demonstrate that our method outperforms Mueller et al. which is the state of the art work dealing with egocentric hand-object interaction problems on the GANerated dataset. To show the ability to preserve the semantic information of our method, we also report the performance of grasp type classification on GUN-71 dataset and outperforms the benchmark by only using the predicted 3-d hand pose.