A key challenge of learning a visual representation for the 3D high fidelity geometry of dressed humans lies in the limited availability of the ground truth data (e.g., 3D scanned models), which results in the performance degradation of 3D human reconstruction when applying to real-world imagery. We address this challenge by leveraging a new data resource: a number of social media dance videos that span diverse appearance, clothing styles, performances, and identities. Each video depicts dynamic movements of the body and clothes of a single person while lacking the 3D ground truth geometry. To learn a visual representation from these videos, we present a new self-supervised learning method to use the local transformation that warps the predicted local geometry of the person from an image to that of another image at a different time instant. This allows self-supervision by enforcing a temporal coherence over the predictions. In addition, we jointly learn the depths along with the surface normals that are highly responsive to local texture, wrinkle, and shade by maximizing their geometric consistency. Our method is end-to-end trainable, resulting in high fidelity depth estimation that predicts fine geometry faithful to the input real image. We further provide a theoretical bound of self-supervised learning via an uncertainty analysis that characterizes the performance of the self-supervised learning without training. We demonstrate that our method outperforms the state-of-the-art human depth estimation and human shape recovery approaches on both real and rendered images.

本文考虑了一个移动机器人的轨迹计划，该机器人在遥远的通信节点对之间持续中继数据。数据在每个源处积聚，机器人必须移动到适当的位置，以使数据卸载到相应的目的地。机器人需要最大程度地减少数据在维修之前在源等待的平均时间。我们有兴趣找到由1）位置组成的最佳机器人路由策略，该位置在该位置停止继电器（继电器位置）和2）确定对配对的序列的条件过渡概率。我们首先将这个问题作为一个非凸面问题，可在中继位置和过渡概率上进行优化。为了找到近似解决方案，我们提出了一种新型算法，该算法交替优化继电器位置和过渡概率。对于前者，我们找到了非凸vex继电器区域的有效凸线分区，然后制定混合校准二阶锥体问题。对于后者，我们通过顺序最小二乘编程找到最佳的过渡概率。我们广泛分析了所提出的方法，并在数学上表征了与机器人的长期能耗和服务速率相关的重要系统属性。最后，通过使用真实的通道参数进行广泛的仿真，我们验证了方法的功效。