  Our work addresses the problem of egocentric human pose estimation from
downwards-facing cameras on head-mounted devices (HMD). This presents a
challenging scenario, as parts of the body often fall outside of the image or
are occluded. Previous solutions minimize this problem by using fish-eye camera
lenses to capture a wider view, but these can present hardware design issues.
They also predict 2D heat-maps per joint and lift them to 3D space to deal with
self-occlusions, but this requires large network architectures which are
impractical to deploy on resource-constrained HMDs. We predict pose from images
captured with conventional rectilinear camera lenses. This resolves hardware
design issues, but means body parts are often out of frame. As such, we
directly regress probabilistic joint rotations represented as matrix Fisher
distributions for a parameterized body model. This allows us to quantify pose
uncertainties and explain out-of-frame or occluded joints. This also removes
the need to compute 2D heat-maps and allows for simplified DNN architectures
which require less compute. Given the lack of egocentric datasets using
rectilinear camera lenses, we introduce the SynthEgo dataset, a synthetic
dataset with 60K stereo images containing high diversity of pose, shape,
clothing and skin tone. Our approach achieves state-of-the-art results for this
challenging configuration, reducing mean per-joint position error by 23%
overall and 58% for the lower body. Our architecture also has eight times fewer
parameters and runs twice as fast as the current state-of-the-art. Experiments
show that training on our synthetic dataset leads to good generalization to
real world images without fine-tuning.
