  We present a method for improving human design of chairs. The goal of the
method is generating enormous chair candidates in order to facilitate human
designer by creating sketches and 3d models accordingly based on the generated
chair design. It consists of an image synthesis module, which learns the
underlying distribution of training dataset, a super-resolution module, which
improve quality of generated image and human involvements. Finally, we manually
pick one of the generated candidates to create a real life chair for
illustration.
