  We present Recurrent Fitting (ReFit), a neural network architecture for
single-image, parametric 3D human reconstruction. ReFit learns a
feedback-update loop that mirrors the strategy of solving an inverse problem
through optimization. At each iterative step, it reprojects keypoints from the
human model to feature maps to query feedback, and uses a recurrent-based
updater to adjust the model to fit the image better. Because ReFit encodes
strong knowledge of the inverse problem, it is faster to train than previous
regression models. At the same time, ReFit improves state-of-the-art
performance on standard benchmarks. Moreover, ReFit applies to other
optimization settings, such as multi-view fitting and single-view shape
fitting. Project website: https://yufu-wang.github.io/refit_humans/
