  We consider the problem of source-free unsupervised category-level pose
estimation from only RGB images to a target domain without any access to source
domain data or 3D annotations during adaptation. Collecting and annotating
real-world 3D data and corresponding images is laborious, expensive, yet
unavoidable process, since even 3D pose domain adaptation methods require 3D
data in the target domain. We introduce 3DUDA, a method capable of adapting to
a nuisance-ridden target domain without 3D or depth data. Our key insight stems
from the observation that specific object subparts remain stable across
out-of-domain (OOD) scenarios, enabling strategic utilization of these
invariant subcomponents for effective model updates. We represent object
categories as simple cuboid meshes, and harness a generative model of neural
feature activations modeled at each mesh vertex learnt using differential
rendering. We focus on individual locally robust mesh vertex features and
iteratively update them based on their proximity to corresponding features in
the target domain even when the global pose is not correct. Our model is then
trained in an EM fashion, alternating between updating the vertex features and
the feature extractor. We show that our method simulates fine-tuning on a
global pseudo-labeled dataset under mild assumptions, which converges to the
target domain asymptotically. Through extensive empirical validation, including
a complex extreme UDA setup which combines real nuisances, synthetic noise, and
occlusion, we demonstrate the potency of our simple approach in addressing the
domain shift challenge and significantly improving pose estimation accuracy.
