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.

Zoom in to the details of human-centric videos

Presenting high-resolution (HR) human appearance is always critical for the human-centric videos. However, current imagery equipment can hardly capture HR details all the time. Existing super-resolution algorithms barely mitigate the problem by only considering universal and low-level priors of im-age patches. In contrast, our algorithm is under bias towards the human body super-resolution by taking advantage of high-level prior defined by HR human appearance. Firstly, a motion analysis module extracts inherent motion pattern from the HR reference video to refine the pose estimation of the low-resolution (LR) sequence. Furthermore, a human body reconstruction module maps the HR texture in the reference frames onto a 3D mesh model. Consequently, the input LR videos get super-resolved HR human sequences are generated conditioned on the original LR videos as well as few HR reference frames. Experiments on an existing dataset and real-world data captured by hybrid cameras show that our approach generates superior visual quality of human body compared with the traditional method.