Heatmap representations have formed the basis of 2D human pose estimation
systems for many years, but their generalizations for 3D pose have only
recently been considered. This includes 2.5D volumetric heatmaps, whose X and Y
axes correspond to image space and the Z axis to metric depth around the
subject. To obtain metric-scale predictions, these methods must include a
separate, explicit post-processing step to resolve scale ambiguity. Further,
they cannot encode body joint positions outside of the image boundaries,
leading to incomplete pose estimates in case of image truncation. We address
these limitations by proposing metric-scale truncation-robust (MeTRo)
volumetric heatmaps, whose dimensions are defined in metric 3D space near the
subject, instead of being aligned with image space. We train a
fully-convolutional network to estimate such heatmaps from monocular RGB in an
end-to-end manner. This reinterpretation of the heatmap dimensions allows us to
estimate complete metric-scale poses without test-time knowledge of the focal
length or person distance and without relying on anthropometric heuristics in
post-processing. Furthermore, as the image space is decoupled from the heatmap
space, the network can learn to reason about joints beyond the image boundary.
Using ResNet-50 without any additional learned layers, we obtain
state-of-the-art results on the Human3.6M and MPI-INF-3DHP benchmarks. As our
method is simple and fast, it can become a useful component for real-time
top-down multi-person pose estimation systems. We make our code publicly
available to facilitate further research (see
this https URL).

本研究中，我们提出了一种度量尺度的截断鲁棒体素热力图，该热力图的维度定义在靠近主体的度量 3D 空间中，使我们能够在没有测试时间知识的情况下估计完整的度量尺度姿势，同时优于现有人体姿势估计标准，具有实时性。