MotionTrace: IMU-based Field of View Prediction for Smartphone AR Interactions
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916346000834560 |
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| author | Islam, Rahul Xu, Vasco Ahuja, Karan |
| author_facet | Islam, Rahul Xu, Vasco Ahuja, Karan |
| contents | For handheld smartphone AR interactions, bandwidth is a critical constraint. Streaming techniques have been developed to provide a seamless and high-quality user experience despite these challenges. To optimize streaming performance in smartphone-based AR, accurate prediction of the user's field of view is essential. This prediction allows the system to prioritize loading digital content that the user is likely to engage with, enhancing the overall interactivity and immersion of the AR experience. In this paper, we present MotionTrace, a method for predicting the user's field of view using a smartphone's inertial sensor. This method continuously estimates the user's hand position in 3D-space to localize the phone position. We evaluated MotionTrace over future hand positions at 50, 100, 200, 400, and 800ms time horizons using the large motion capture (AMASS) and smartphone-based full-body pose estimation (Pose-on-the-Go) datasets. We found that our method can estimate the future phone position of the user with an average MSE between 0.11 - 143.62 mm across different time horizons. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_01850 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MotionTrace: IMU-based Field of View Prediction for Smartphone AR Interactions Islam, Rahul Xu, Vasco Ahuja, Karan Human-Computer Interaction For handheld smartphone AR interactions, bandwidth is a critical constraint. Streaming techniques have been developed to provide a seamless and high-quality user experience despite these challenges. To optimize streaming performance in smartphone-based AR, accurate prediction of the user's field of view is essential. This prediction allows the system to prioritize loading digital content that the user is likely to engage with, enhancing the overall interactivity and immersion of the AR experience. In this paper, we present MotionTrace, a method for predicting the user's field of view using a smartphone's inertial sensor. This method continuously estimates the user's hand position in 3D-space to localize the phone position. We evaluated MotionTrace over future hand positions at 50, 100, 200, 400, and 800ms time horizons using the large motion capture (AMASS) and smartphone-based full-body pose estimation (Pose-on-the-Go) datasets. We found that our method can estimate the future phone position of the user with an average MSE between 0.11 - 143.62 mm across different time horizons. |
| title | MotionTrace: IMU-based Field of View Prediction for Smartphone AR Interactions |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2408.01850 |