EMAG: Ego-motion Aware and Generalizable 2D Hand Forecasting from Egocentric Videos
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| Format: | Preprint |
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2024
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| _version_ | 1866929470827397120 |
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| author | Hatano, Masashi Hachiuma, Ryo Saito, Hideo |
| author_facet | Hatano, Masashi Hachiuma, Ryo Saito, Hideo |
| contents | Predicting future human behavior from egocentric videos is a challenging but critical task for human intention understanding. Existing methods for forecasting 2D hand positions rely on visual representations and mainly focus on hand-object interactions. In this paper, we investigate the hand forecasting task and tackle two significant issues that persist in the existing methods: (1) 2D hand positions in future frames are severely affected by ego-motions in egocentric videos; (2) prediction based on visual information tends to overfit to background or scene textures, posing a challenge for generalization on novel scenes or human behaviors. To solve the aforementioned problems, we propose EMAG, an ego-motion-aware and generalizable 2D hand forecasting method. In response to the first problem, we propose a method that considers ego-motion, represented by a sequence of homography matrices of two consecutive frames. We further leverage modalities such as optical flow, trajectories of hands and interacting objects, and ego-motions, thereby alleviating the second issue. Extensive experiments on two large-scale egocentric video datasets, Ego4D and EPIC-Kitchens 55, verify the effectiveness of the proposed method. In particular, our model outperforms prior methods by 1.7% and 7.0% on intra and cross-dataset evaluations, respectively. Project page: https://masashi-hatano.github.io/EMAG/ |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_20030 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EMAG: Ego-motion Aware and Generalizable 2D Hand Forecasting from Egocentric Videos Hatano, Masashi Hachiuma, Ryo Saito, Hideo Computer Vision and Pattern Recognition Predicting future human behavior from egocentric videos is a challenging but critical task for human intention understanding. Existing methods for forecasting 2D hand positions rely on visual representations and mainly focus on hand-object interactions. In this paper, we investigate the hand forecasting task and tackle two significant issues that persist in the existing methods: (1) 2D hand positions in future frames are severely affected by ego-motions in egocentric videos; (2) prediction based on visual information tends to overfit to background or scene textures, posing a challenge for generalization on novel scenes or human behaviors. To solve the aforementioned problems, we propose EMAG, an ego-motion-aware and generalizable 2D hand forecasting method. In response to the first problem, we propose a method that considers ego-motion, represented by a sequence of homography matrices of two consecutive frames. We further leverage modalities such as optical flow, trajectories of hands and interacting objects, and ego-motions, thereby alleviating the second issue. Extensive experiments on two large-scale egocentric video datasets, Ego4D and EPIC-Kitchens 55, verify the effectiveness of the proposed method. In particular, our model outperforms prior methods by 1.7% and 7.0% on intra and cross-dataset evaluations, respectively. Project page: https://masashi-hatano.github.io/EMAG/ |
| title | EMAG: Ego-motion Aware and Generalizable 2D Hand Forecasting from Egocentric Videos |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.20030 |