  Traditionally, monocular 3D human pose estimation employs a machine learning
model to predict the most likely 3D pose for a given input image. However, a
single image can be highly ambiguous and induces multiple plausible solutions
for the 2D-3D lifting step which results in overly confident 3D pose
predictors. To this end, we propose \emph{DiffPose}, a conditional diffusion
model, that predicts multiple hypotheses for a given input image. In comparison
to similar approaches, our diffusion model is straightforward and avoids
intensive hyperparameter tuning, complex network structures, mode collapse, and
unstable training. Moreover, we tackle a problem of the common two-step
approach that first estimates a distribution of 2D joint locations via
joint-wise heatmaps and consecutively approximates them based on first- or
second-moment statistics. Since such a simplification of the heatmaps removes
valid information about possibly correct, though labeled unlikely, joint
locations, we propose to represent the heatmaps as a set of 2D joint candidate
samples. To extract information about the original distribution from these
samples we introduce our \emph{embedding transformer} that conditions the
diffusion model. Experimentally, we show that DiffPose slightly improves upon
the state of the art for multi-hypothesis pose estimation for simple poses and
outperforms it by a large margin for highly ambiguous poses.
