We consider the problem of obtaining dense 3D reconstructions of humans from
single and partially occluded views. In such cases, the visual evidence is
usually insufficient to identify a 3D reconstruction uniquely, so we aim at
recovering several plausible reconstructions compatible with the input data. We
suggest that ambiguities can be modelled more effectively by parametrizing the
possible body shapes and poses via a suitable 3D model, such as SMPL for
humans. We propose to learn a multi-hypothesis neural network regressor using a
best-of-M loss, where each of the M hypotheses is constrained to lie on a
manifold of plausible human poses by means of a generative model. We show that
our method outperforms alternative approaches in ambiguous pose recovery on
standard benchmarks for 3D humans, and in heavily occluded versions of these
benchmarks.

研究单视图和部分遮挡视图下获取人体的密集三维重建问题，建议通过合适的三维模型参数化人体形状和姿势，学习一种多假设神经网络回归器，约束每个假设位于合理人体姿势的流形上，并且在标准 3D 人体基准和重度遮挡版本中，该方法表现出优异的处理模糊姿态恢复的性能。