  We propose a method to estimate 3D human poses from substantially blurred
images. The key idea is to tackle the inverse problem of image deblurring by
modeling the forward problem with a 3D human model, a texture map, and a
sequence of poses to describe human motion. The blurring process is then
modeled by a temporal image aggregation step. Using a differentiable renderer,
we can solve the inverse problem by backpropagating the pixel-wise reprojection
error to recover the best human motion representation that explains a single or
multiple input images. Since the image reconstruction loss alone is
insufficient, we present additional regularization terms. To the best of our
knowledge, we present the first method to tackle this problem. Our method
consistently outperforms other methods on significantly blurry inputs since
they lack one or multiple key functionalities that our method unifies, i.e.
image deblurring with sub-frame accuracy and explicit 3D modeling of non-rigid
human motion.
