Furnishing and rendering indoor scenes has been a long-standing task for
interior design, where artists create a conceptual design for the space, build
a 3D model of the space, decorate, and then perform rendering. Although the
task is important, it is tedious and requires tremendous effort. In this paper,
we introduce a new problem of domain-specific indoor scene image synthesis,
namely neural scene decoration. Given a photograph of an empty indoor space and
a list of decorations with layout determined by user, we aim to synthesize a
new image of the same space with desired furnishing and decorations. Neural
scene decoration can be applied to create conceptual interior designs in a
simple yet effective manner. Our attempt to this research problem is a novel
scene generation architecture that transforms an empty scene and an object
layout into a realistic furnished scene photograph. We demonstrate the
performance of our proposed method by comparing it with conditional image
synthesis baselines built upon prevailing image translation approaches both
qualitatively and quantitatively. We conduct extensive experiments to further
validate the plausibility and aesthetics of our generated scenes. Our
implementation is available at
\url{https://github.com/hkust-vgd/neural_scene_decoration}.Comment: ECCV 2022 paper. 14 pages of main content, 4 pages of references, and
11 pages of appendi