Current computer graphics techniques allow us to render almost any object at near photo-realistic quality. However, the standard approach necessitates that the user painstakingly specifies all aspects of the geometric and material properties of the object. This is time-consuming and needs skilled human operators. It is hard to edit the resulting models at anything other than the low level of geometry and materials at which they are specified. Moreover, we cannot edit real photographs without reverse engineering the underlying model and this is very difficult.In this proposal we investigate a radically different pipeline for computer graphics that will allow non-experts to rapidly create and edit photo-realistic two dimensional images of objects. The crux of our approach is to provide the computer with a deeper understanding of the class of objects under consideration. This knowledge (which takes the form of a statistical model) is then leveraged to help the user achieve their goals more easily. The impact of this project is potentially enormous. Such a technology could become a standard tool installed on every home and business computer. Some of the many potential applications are:- Conceptual design. Manufacturing industries often need to sketch new product ideas and refine existing designs. Our system could help a fashion designer produce and manipulate photo-realistic images of new garments.- Clipart objects. Stock images are required for on-line and real-world publishing and these are often sought via search engines (e.g. Google Images). However, the returned results are often not ideal and may be subject to copyright. Our approach will allow the user to design bespoke images to exactly their specifications.- Photo and movie editing. Digital editing of images and movies is commonplace, but requires considerable skill. Our techniques could be used to modify facial expressions in portrait photography or apply digital cosmetics in movie post-production.- Content for virtual worlds. The trend towards larger 'sandbox' environments in video games has created an explosive demand for graphical content. Our system could allow automated or semi-automated creation of photorealistic building facades for a large virtual environment.

Generative Models are AI models that can generate data. Recently researchers have shown that by training these models on large amounts of data (text data from the internet and images) these models learn to understand the regularities of our text and image world so well that they can generate responses to questions and create new images with surprising fidelity. This heralds a new era in which computers can assist humans to carry out tasks more efficiently than ever with significant opportunities for society, science and industry. However, these advances need significant research still -- how to make them train efficiently on different problems, how to understand their reliability and adherence to ethical norms.

Data-driven innovation is transforming every sector of our digital economy (DE) into a de-centralised marketplace; accommodation (AirBnb), transportation (Uber), logistics (Deliveroo), user-generated vs. broadcast content in the creative industries (YouTube). We are witnessing an inexorable shift from classical models centred upon monolithic institutions, to a dynamic and decentralised economy in which anyone is a potential producer and consumer. A gig economy, underpinned by digital products and services co-created through shorter-lived, diverse peer-to-peer engagements. Yet, the platforms that enable this DE are increasingly built on centralised architectures. These are not controlled by society, but by large organisations making commercial decisions far from the social contexts they affect. There is an urgent need to disrupt this relationship, to deliver proper governance that empowers society to take control of the DE and enables people to assert greater agency over the vast centralised silos of data that drive these platforms. We stand on the cusp of a second wave of DE disruption, driven by bleeding edge data-driven technologies (AI) and secure, distributed data sharing infrastructures such as Distributed Ledger Technologies (DLT), in which data is no longer siloed but becomes a fluid, de-centralised commodity shifting power away from tech giants to individuals and de-centralised organisations. This future Decentralised Digital Economy (DDE) enables people and organisations to work together, to trade, and ultimately to trust via frictionless digital interactions free from reliance upon centralised third parties, but often with reliance upon autonomous services. This shift in agency and power is a game changing opportunity for society to take back control over its digital economy - but we have a limited window of opportunity to get it right. We have already witnessed de-centralisation in the financial sector, where the lack of regulation and clear governance of crypto-currencies has proven a double-edged sword, allowing free exchange of value across the globe, but that is coupled with fraudulent company flotations and currency rates rigged by large mining pools. This is a consequence of technology-driven innovation unchecked by socio-economic insight; a lack of knowledge making policy makers impotent in the face of the tech giants. We are now at the tipping point of similar wide-sweeping disruption across all sectors in the DDE, a transformation that will radically redefine our models of value and how it is created, the ways in which we work, and how we use and extract value from our data. DECaDE represents a critical and timely opportunity to shape this emerging de-centralised digital economy (DDE), to develop insights that define a new 21st century model of work and value creation in the DDE, and ensure a prosperous, safe and inclusive society for all. DECaDE is a 60 month centre, comprising 21 people and building upon over 8.6 million pounds of feasibility scale UKRI/EPSRC investments in DLT and Human Data Interaction (HDI) held by the proposing team. DECaDE is a three-way partnership between the Universities of Surrey and Edinburgh, and the Digital Catapult DLT Field Labs. The latter is a full member of the consortium, through which we have co-created this research programme and with whom we will engage in further co-creation of the future DDE through diverse end-users in the public and private sector to support the competitive position of the UK