TSAMT: Time-Series-Analysis-based Motion Transfer among Multiple Cameras

Along with advances in optical sensors is the common practice of building an imaging system with heterogeneous cameras. While high-resolution (HR) videos acquisition and analysis are benefited from hybrid sensors, the intrinsic characteristics of multiple cameras lead to an interesting motion transfer problem. Unfortunately, most of the existing methods provide no theoretical analysis and require intensive training data. In this paper, we propose an algorithm using time series analysis for motion transfer among multiple cameras. Specifically, we firstly identify seasonality in motion data and then build an addictive time series model to extract patterns that could be transferred across cameras. Our approach has a complete and clear mathematical formulation, thus being efficient and interpretable. Through quantitative evaluations on real-world data, we demonstrate the effectiveness of our method. Furthermore, our motion transfer algorithm could combine with and facilitate downstream tasks, e.g., enhancing pose estimation on LR videos with inherent patterns extracted from HR ones. Code is available at https://github.com/IndigoPurple/TSAMT.