Face Aging via Diffusion-based Editing

In this paper, we address the problem of face aging: generating past or future facial images by incorporating age-related changes to the given face. Previous aging methods rely solely on human facial image datasets and are thus constrained by their inherent scale and bias. This restricts their application to a limited generatable age range and the inability to handle large age gaps. We propose FADING, a novel approach to address Face Aging via DIffusion-based editiNG. We go beyond existing methods by leveraging the rich prior of large-scale language-image diffusion models. First, we specialize a pre-trained diffusion model for the task of face age editing by using an age-aware fine-tuning scheme. Next, we invert the input image to latent noise and obtain optimized null text embeddings. Finally, we perform text-guided local age editing via attention control. The quantitative and qualitative analyses demonstrate that our method outperforms existing approaches with respect to aging accuracy, attribute preservation, and aging quality.Comment: accepted at BMVC 202

Application Directed Synthesis of Multifunctional Fullerene Derivatives

This thesis presents work on the synthesis and characterisation of functionalised fullerene derivatives designed specifically for applications in energy storage, supramolecular assembly and lithographic patterning. Chapter 1 provides an introduction to fullerene, fullerene chemistry, the examples of structurally complex fullerene derivatives and their corresponding applications. Chapter 2 describes the synthesis of highly soluble fullerene derivatives as charge carriers for redox flow batteries, achieving a remarkably high solubility of ~336 mM in oDCB, and exhibiting a wide potential window, 1.78 V, in an electrolyte consisting of oDCB/tetrabutylammonium tetrafluoroborate. The synthesis of a series of tris-fullerene CTG molecules that can be used for developing supramolecular arrays is introduced in Chapter 3, in which a synthetic strategy of using tris-amino acid derivatised CTG as the starting material in a one-pot Prato reaction was proposed and investigated to yield the target molecule. In Chapter 4, a fullerene-platinum complex is explored as a resist material to develop the lithographic pattern, in which a sub-13 nm line width was achieved. The presence of Pt atoms enhances the secondary electron scattering and thus increased the energy deposition efficiency resulting in better lithographic patterning capability. Overall, this thesis acts as a guide to efficient and targeted fullerene derivative synthesis, providing insight and strategy into fullerene functionalisation which will help push forward the exploitation of fullerene as a nanosized building block to be utilised in applied, functional materials in the future

Adaptive Graphical Model Network for 2D Handpose Estimation

In this paper, we propose a new architecture called Adaptive Graphical Model Network (AGMN) to tackle the task of 2D hand pose estimation from a monocular RGB image. The AGMN consists of two branches of deep convolutional neural networks for calculating unary and pairwise potential functions, followed by a graphical model inference module for integrating unary and pairwise potentials. Unlike existing architectures proposed to combine DCNNs with graphical models, our AGMN is novel in that the parameters of its graphical model are conditioned on and fully adaptive to individual input images. Experiments show that our approach outperforms the state-of-the-art method used in 2D hand keypoints estimation by a notable margin on two public datasets.Comment: 30th British Machine Vision Conference (BMVC