  This paper focuses on the problem of 3D human reconstruction from 2D
evidence. Although this is an inherently ambiguous problem, the majority of
recent works avoid the uncertainty modeling and typically regress a single
estimate for a given input. In contrast to that, in this work, we propose to
embrace the reconstruction ambiguity and we recast the problem as learning a
mapping from the input to a distribution of plausible 3D poses. Our approach is
based on the normalizing flows model and offers a series of advantages. For
conventional applications, where a single 3D estimate is required, our
formulation allows for efficient mode computation. Using the mode leads to
performance that is comparable with the state of the art among deterministic
unimodal regression models. Simultaneously, since we have access to the
likelihood of each sample, we demonstrate that our model is useful in a series
of downstream tasks, where we leverage the probabilistic nature of the
prediction as a tool for more accurate estimation. These tasks include
reconstruction from multiple uncalibrated views, as well as human model
fitting, where our model acts as a powerful image-based prior for mesh
recovery. Our results validate the importance of probabilistic modeling, and
indicate state-of-the-art performance across a variety of settings. Code and
models are available at: https://www.seas.upenn.edu/~nkolot/projects/prohmr.
