Estimating 3D from 2D is one of the central tasks in computer vision. In this work, we consider the monocular setting, i.e. single-view input, for 3D human pose estimation (HPE). Here, the task is to predict a 3D point set of human skeletal joints from a single 2D input image. While by definition this is an ill-posed problem, recent work has presented methods that solve it with up to several-centimetre error. Typically, these methods employ a two-step approach, where the first step is to detect the 2D skeletal joints in the input image, followed by the step of 2D-to-3D lifting. We find that common lifting models fail when encountering a rotated input. We argue that learning a single human pose along with its in-plane rotations is considerably easier and more geometrically grounded than directly learning a point-to-point mapping. Furthermore, our intuition is that endowing the model with the notion of rotation equivariance without explicitly constraining its parameter space should lead to a more straightforward learning process than one with equivariance by design. Utilising the common HPE benchmarks, we confirm that the 2D rotation equivariance per se improves the model performance on human poses akin to rotations in the image plane, and can be efficiently and straightforwardly learned by augmentation, outperforming state-of-the-art equivariant-by-design methods.

翻译：从二维数据估计三维结构是计算机视觉的核心任务之一。本研究聚焦于单目（即单视图输入）条件下的三维人体姿态估计。该任务旨在从单幅二维输入图像预测人体骨骼关节的三维点集。尽管该问题本质上是病态的，近期研究已提出误差可控制在数厘米级的解决方法。典型方法采用两步策略：首先在输入图像中检测二维骨骼关节，随后进行二维到三维的提升映射。我们发现现有提升模型在处理旋转输入时普遍失效。我们认为，学习单一人体姿态及其平面内旋转比直接学习点对点映射更为简易且几何意义明确。此外，我们的直觉表明：在不显式约束参数空间的前提下赋予模型旋转等变性概念，将比通过设计实现等变性的模型获得更直接的学习过程。通过使用通用的人体姿态估计基准测试，我们证实二维旋转等变性本身能提升模型对图像平面内旋转人体姿态的处理性能，且通过数据增强即可高效直接地习得该特性，其表现优于当前最先进的等变性设计方法。