论文标题
放大以人为中心的视频的细节
Zoom in to the details of human-centric videos
论文作者
论文摘要
呈现高分辨率（HR）的外观对于以人为中心的视频始终至关重要。但是，当前的图像设备几乎无法始终捕获人力资源细节。现有的超分辨率算法几乎没有通过考虑通用和低水平的IM-AGE贴片的先验来减轻问题。相比之下，我们的算法通过利用人力资源人力人力资源人的外观定义的高级先验来对人体超级分辨率有偏见。首先，运动分析模块从HR参考视频中提取固有的运动模式，以完善低分辨率（LR）序列的姿势估计。此外，人体重建模块将参考帧中的人力资源纹理映射到3D网格模型上。因此，输入LR视频获得了超级分辨的人力资源序列，该序列是在原始LR视频以及很少的HR参考帧中生成的。与传统方法相比，在混合摄像头捕获的现有数据集和现实世界数据的实验表明，人体的视觉质量卓越。
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.
