  With a proliferation of generic domain-adaptation approaches, we report a
simple yet effective technique for learning difficult per-pixel 2.5D and 3D
regression representations of articulated people. We obtained strong
sim-to-real domain generalization for the 2.5D DensePose estimation task and
the 3D human surface normal estimation task. On the multi-person DensePose
MSCOCO benchmark, our approach outperforms the state-of-the-art methods which
are trained on real images that are densely labelled. This is an important
result since obtaining human manifold's intrinsic uv coordinates on real images
is time consuming and prone to labeling noise. Additionally, we present our
model's 3D surface normal predictions on the MSCOCO dataset that lacks any real
3D surface normal labels. The key to our approach is to mitigate the
"Inter-domain Covariate Shift" with a carefully selected training batch from a
mixture of domain samples, a deep batch-normalized residual network, and a
modified multi-task learning objective. Our approach is complementary to
existing domain-adaptation techniques and can be applied to other dense
per-pixel pose estimation problems.
