Neural Scene Decoration from a Single Photograph
Hong-Wing Pang, Yingshu Chen, Phuoc-Hieu Le, Binh-Son Hua, Duc Thanh, Nguyen, Sai-Kit Yeung

TL;DR
This paper introduces neural scene decoration, a novel method for synthesizing furnished indoor scene images from an empty photograph and user-defined layout, simplifying interior design visualization.
Contribution
It proposes a new scene generation architecture that transforms empty scenes and object layouts into realistic furnished images, advancing automated interior scene synthesis.
Findings
Outperforms baseline image translation methods qualitatively and quantitatively
Generates plausible and aesthetically pleasing interior scenes
Validated through extensive experiments
Abstract
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…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Computer Graphics and Visualization Techniques · Advanced Vision and Imaging
