  Despite recent advances in 3D human mesh reconstruction, domain gap between
training and test data is still a major challenge. Several prior works tackle
the domain gap problem via test-time adaptation that fine-tunes a network
relying on 2D evidence (e.g., 2D human keypoints) from test images. However,
the high reliance on 2D evidence during adaptation causes two major issues.
First, 2D evidence induces depth ambiguity, preventing the learning of accurate
3D human geometry. Second, 2D evidence is noisy or partially non-existent
during test time, and such imperfect 2D evidence leads to erroneous adaptation.
To overcome the above issues, we introduce CycleAdapt, which cyclically adapts
two networks: a human mesh reconstruction network (HMRNet) and a human motion
denoising network (MDNet), given a test video. In our framework, to alleviate
high reliance on 2D evidence, we fully supervise HMRNet with generated 3D
supervision targets by MDNet. Our cyclic adaptation scheme progressively
elaborates the 3D supervision targets, which compensate for imperfect 2D
evidence. As a result, our CycleAdapt achieves state-of-the-art performance
compared to previous test-time adaptation methods. The codes are available at
https://github.com/hygenie1228/CycleAdapt_RELEASE.
