  We present Pix2Repair, an automated shape repair approach that generates
restoration shapes from images to repair fractured objects. Prior repair
approaches require a high-resolution watertight 3D mesh of the fractured object
as input. Input 3D meshes must be obtained using expensive 3D scanners, and
scanned meshes require manual cleanup, limiting accessibility and scalability.
Pix2Repair takes an image of the fractured object as input and automatically
generates a 3D printable restoration shape. We contribute a novel shape
function that deconstructs a latent code representing the fractured object into
a complete shape and a break surface. We also introduce Fantastic Breaks
Imaged, the first large-scale dataset of 11,653 real-world images of fractured
objects for training and evaluating image-based shape repair approaches. Our
dataset contains images of objects from Fantastic Breaks, complete with rich
annotations. We show restorations for real fractures from our dataset, and for
synthetic fractures from the Geometric Breaks and Breaking Bad datasets. Our
approach outperforms shape completion approaches adapted for shape repair in
terms of chamfer distance, normal consistency, and percent restorations
generated.
