  We propose pose-guided multiplane image (MPI) synthesis which can render an
animatable character in real scenes with photorealistic quality. We use a
portable camera rig to capture the multi-view images along with the driving
signal for the moving subject. Our method generalizes the image-to-image
translation paradigm, which translates the human pose to a 3D scene
representation -- MPIs that can be rendered in free viewpoints, using the
multi-views captures as supervision. To fully cultivate the potential of MPI,
we propose depth-adaptive MPI which can be learned using variable exposure
images while being robust to inaccurate camera registration. Our method
demonstrates advantageous novel-view synthesis quality over the
state-of-the-art approaches for characters with challenging motions. Moreover,
the proposed method is generalizable to novel combinations of training poses
and can be explicitly controlled. Our method achieves such expressive and
animatable character rendering all in real time, serving as a promising
solution for practical applications.
