Decorating Indoor Scenes with Generative Neural Networks

MPhil Thesis Defence


Title: "Decorating Indoor Scenes with Generative Neural Networks"

By

Mr. Hong Wing PANG


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 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, exten experiments are conducted to further 
validate the plausibility and aesthetics of the generated results based on 
the proposed approach.


Date:  			Friday, 26 August 2022

Time:			4:00pm - 6:00pm

Zoom Meeting: 
https://hkust.zoom.us/j/99730348000?pwd=SG45MzZTRUJwQ2dCTHlma2Z5NUNrUT09

Committee Members:	Dr. Sai-Kit Yeung (Supervisor)
 			Prof. Pedro Sander (Chairperson)
 			Prof. Chiew-Lan Tai


**** ALL are Welcome ****