THESIS
2022
1 online resource (ix, 51 pages) : color illustrations
Abstract
Furnishing and rendering indoor scenes has been a longstanding task for interior design, where
artists create a conceptual design for the space, build a 3D model of the space, decorates,
and then performs rendering. Traditionally, the task is typically performed using professional
3D CAD design software, which is a tedious process and requires extensive prior knowledge
and experience. Hence, we introduce the task of neural scene decoration (NSD), utilizing
generative neural networks in assisting domain-specific scene synthesis.
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
interio...[
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Furnishing and rendering indoor scenes has been a longstanding task for interior design, where
artists create a conceptual design for the space, build a 3D model of the space, decorates,
and then performs rendering. Traditionally, the task is typically performed using professional
3D CAD design software, which is a tedious process and requires extensive prior knowledge
and experience. Hence, we introduce the task of neural scene decoration (NSD), utilizing
generative neural networks in assisting domain-specific scene synthesis.
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. In this work, a novel approach is proposed
towards solving this problem, based on training a conditional GAN network. The performance
of the proposed method is illustrated by comparing it with baselines built upon prevailing
image translation approaches both qualitatively and quantitatively. In addition, extensive
experiments are conducted to further validate the plausibility and aesthetics of the generated
results based on the proposed approach.
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