  We address the novel task of jointly reconstructing the 3D shape, texture,
and motion of an object from a single motion-blurred image. While previous
approaches address the deblurring problem only in the 2D image domain, our
proposed rigorous modeling of all object properties in the 3D domain enables
the correct description of arbitrary object motion. This leads to significantly
better image decomposition and sharper deblurring results. We model the
observed appearance of a motion-blurred object as a combination of the
background and a 3D object with constant translation and rotation. Our method
minimizes a loss on reconstructing the input image via differentiable rendering
with suitable regularizers. This enables estimating the textured 3D mesh of the
blurred object with high fidelity. Our method substantially outperforms
competing approaches on several benchmarks for fast moving objects deblurring.
Qualitative results show that the reconstructed 3D mesh generates high-quality
temporal super-resolution and novel views of the deblurred object.
