Karnewar, Animesh; (2024) Towards Computationally Efficient, Photorealistic, and Scalable 3D Generative Modelling. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

GM (Generative Modelling) is a class of self supervised Machine Learning which finds applications in synthetic data generation, semantic representation learning, and various creative and artistic fields. GM (aka. Generative AI) seemingly holds the potential for the next breakthrough in AI; of which, the recent successes in LLMs, text-to-image synthesis and text to-video synthesis serve as formidable testament. The way these generative models have revolutionized the process of 2D content creation, we can expect that 3D generative modelling will also contribute significantly towards simplifying the process of 3D content creation. However, it is non-trivial to extend the 2D generative algorithms to operate on 3D data managing various factors such as the inherent data-sparsity, the growing memory requirements, and the computational complexity. The application of Generative Modelling to 3D data is made even harder due to the pertaining challenges: firstly, finding a large quantity of 3D training data is much more complex than 2D images; and secondly, there is no de-facto representation for 3D assets, where various different representations such as point-clouds, meshes, voxel grids, neural (MLP)s, etc. are used depending on the application. Thus, with the goal of ultimately enabling 3D Generative Models, and considering the aforementioned challenges, I propose this thesis which makes substantial strides “Towards Computationally Efficient, Photorealistic, and Scalable 3D Generative Modelling”.

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