A Survey On Text-to-3D Contents Generation In The Wild

PhD Qualifying Examination


Title: "A Survey On Text-to-3D Contents Generation In The Wild"

by

Miss Chenhan JIANG


Abstract:

3D content creation plays a vital role in various applications, such as gaming, 
robotics simulation, and virtual reality. However, the process is 
labor-intensive and time-consuming, requiring skilled designers to invest 
considerable effort in creating a single 3D asset. To address this challenge, 
text-to-3D generation technologies have emerged as a promising solution for 
automating 3D creation. Leveraging the success of large vision language models, 
these techniques aim to generate 3D content based on textual descriptions. 
Despite recent advancements in this area, existing solutions still face 
significant limitations in terms of generation quality and efficiency.

In this survey, we conduct an in-depth investigation of the latest text-to-3D 
creation methods. We provide a comprehensive background on text-to-3D creation, 
including discussions on datasets employed in training and evaluation metrics 
used to assess the quality of generated 3D models. Then, we delve into the 
various 3D representations that serve as the foundation for the 3D generation 
process. Furthermore, we present a thorough comparison of the rapidly growing 
literature on generation pipelines, categorizing them into feedforward 
generators, optimization-based generation, and multi- view reconstruction 
approaches. By examining the strengths and weaknesses of these methods, we aim 
to shed light on their respective capabilities and limitations. Lastly, we 
point out several promising avenues for future research. With this survey, we 
hope to inspire researchers further to explore the potential of 
text-conditioned 3D content creation.


Date:                   Friday, 10 May 2024

Time:                   2:00pm - 4:00pm

Venue:                  Room 5508
                        Lifts 25/26

Committee Members:      Prof. Dit-Yan Yeung (Supervisor)
                        Prof. Chi-Keung Tang (Chairperson)
                        Dr. Long Chen
                        Dr. Dan Xu