  We propose a framework that can deform an object in a 2D image as it exists
in 3D space. Most existing methods for 3D-aware image manipulation are limited
to (1) only changing the global scene information or depth, or (2) manipulating
an object of specific categories. In this paper, we present a 3D-aware image
deformation method with minimal restrictions on shape category and deformation
type. While our framework leverages 2D-to-3D reconstruction, we argue that
reconstruction is not sufficient for realistic deformations due to the
vulnerability to topological errors. Thus, we propose to take a supervised
learning-based approach to predict the shape Laplacian of the underlying volume
of a 3D reconstruction represented as a point cloud. Given the deformation
energy calculated using the predicted shape Laplacian and user-defined
deformation handles (e.g., keypoints), we obtain bounded biharmonic weights to
model plausible handle-based image deformation. In the experiments, we present
our results of deforming 2D character and clothed human images. We also
quantitatively show that our approach can produce more accurate deformation
weights compared to alternative methods (i.e., mesh reconstruction and point
cloud Laplacian methods).
