Papers › Joint Hand Motion and Interaction Hotspots Prediction from Egocentric Videos

Joint Hand Motion and Interaction Hotspots Prediction from Egocentric Videos

4 Apr 2022CVPR 2022 1arXiv:2204.01696archive 2025-07-28

Shaowei Liu, Subarna Tripathi, Somdeb Majumdar, Xiaolong Wang

We propose to forecast future hand-object interactions given an egocentric video. Instead of predicting action labels or pixels, we directly predict the hand motion trajectory and the future contact points on the next active object (i.e., interaction hotspots). This relatively low-dimensional representation provides a concrete description of future interactions. To tackle this task, we first provide an automatic way to collect trajectory and hotspots labels on large-scale data. We then use this data to train an Object-Centric Transformer (OCT) model for prediction. Our model performs hand and object interaction reasoning via the self-attention mechanism in Transformers. OCT also provides a probabilistic framework to sample the future trajectory and hotspots to handle uncertainty in prediction. We perform experiments on the Epic-Kitchens-55, Epic-Kitchens-100, and EGTEA Gaze+ datasets, and show that OCT significantly outperforms state-of-the-art approaches by a large margin. Project page is available at https://stevenlsw.github.io/hoi-forecast .

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