  Human pose and shape (HPS) estimation methods achieve remarkable results.
However, current HPS benchmarks are mostly designed to test models in scenarios
that are similar to the training data. This can lead to critical situations in
real-world applications when the observed data differs significantly from the
training data and hence is out-of-distribution (OOD). It is therefore important
to test and improve the OOD robustness of HPS methods. To address this
fundamental problem, we develop a simulator that can be controlled in a
fine-grained manner using interpretable parameters to explore the manifold of
images of human pose, e.g. by varying poses, shapes, and clothes. We introduce
a learning-based testing method, termed PoseExaminer, that automatically
diagnoses HPS algorithms by searching over the parameter space of human pose
images to find the failure modes. Our strategy for exploring this
high-dimensional parameter space is a multi-agent reinforcement learning
system, in which the agents collaborate to explore different parts of the
parameter space. We show that our PoseExaminer discovers a variety of
limitations in current state-of-the-art models that are relevant in real-world
scenarios but are missed by current benchmarks. For example, it finds large
regions of realistic human poses that are not predicted correctly, as well as
reduced performance for humans with skinny and corpulent body shapes. In
addition, we show that fine-tuning HPS methods by exploiting the failure modes
found by PoseExaminer improve their robustness and even their performance on
standard benchmarks by a significant margin. The code are available for
research purposes.
