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With a proliferation of generic domain-adaptation approaches, we report asimple yet effective technique for learning difficult per-pixel 2.5D and 3Dregression representations of articulated people. We obtained strongsim-to-real domain generalization for the 2.5D DensePose estimation task andthe 3D human surface normal estimation task. On the multi-person DensePoseMSCOCO benchmark, our approach outperforms the state-of-the-art methods whichare trained on real images that are densely labelled. This is an importantresult since obtaining human manifold's intrinsic uv coordinates on real imagesis time consuming and prone to labeling noise. Additionally, we present ourmodel's 3D surface normal predictions on the MSCOCO dataset that lacks any real3D surface normal labels. The key to our approach is to mitigate the"Inter-domain Covariate Shift" with a carefully selected training batch from amixture of domain samples, a deep batch-normalized residual network, and amodified multi-task learning objective. Our approach is complementary toexisting domain-adaptation techniques and can be applied to other denseper-pixel pose estimation problems.

SimPose: Effectively Learning DensePose and Surface Normals of People from Simulated Data