论文标题
对合成数据集的无标记人类运动的深度学习技术的评论
A Review of Deep Learning Techniques for Markerless Human Motion on Synthetic Datasets
论文作者
论文摘要
近年来，无标记运动捕获已成为计算机视觉研究的积极研究领域。它的广泛应用是在各种领域中闻名的，包括计算机动画，人类运动分析，生物医学研究，虚拟现实和运动科学。估计人类姿势最近在计算机视觉社区中引起了越来越多的关注，但是由于不确定性的深度和缺乏合成数据集，这是一项艰巨的任务。最近提出了各种方法来解决此问题，其中许多方法基于深度学习。他们主要致力于提高具有重大进展的现有基准的性能，尤其是2D图像。基于强大的深度学习技术和最近收集的现实世界数据集，我们探索了一个模型，该模型可以仅基于2D图像来预测动画的骨架。使用不同的身体形状从简单到复杂的不同身体形状，从不同的现实世界数据集生成的帧。实现过程使用自己的数据集上使用DeepLabcut来执行许多必要的步骤，然后使用输入帧来训练模型。输出是人类运动的动画骨架。复合数据集和其他结果是深层模型的“基础真相”。
Markerless motion capture has become an active field of research in computer vision in recent years. Its extensive applications are known in a great variety of fields, including computer animation, human motion analysis, biomedical research, virtual reality, and sports science. Estimating human posture has recently gained increasing attention in the computer vision community, but due to the depth of uncertainty and the lack of the synthetic datasets, it is a challenging task. Various approaches have recently been proposed to solve this problem, many of which are based on deep learning. They are primarily focused on improving the performance of existing benchmarks with significant advances, especially 2D images. Based on powerful deep learning techniques and recently collected real-world datasets, we explored a model that can predict the skeleton of an animation based solely on 2D images. Frames generated from different real-world datasets with synthesized poses using different body shapes from simple to complex. The implementation process uses DeepLabCut on its own dataset to perform many necessary steps, then use the input frames to train the model. The output is an animated skeleton for human movement. The composite dataset and other results are the "ground truth" of the deep model.
