ERC Consolidator Grant · 2024
Learning to Create Virtual Worlds
In recent years, we have seen a revolution of learning methods that generate highly-realistic images, such as generative adversarial neural networks, autoregressive methods, or diffusion models (e.g., DALL-E, Stable Diffusion, Runway, etc). Unfortunately, the vast majority of these methods are tailored towards the 2D image domain, while their respective 3D counterparts – 3D models that fuel computer graphics applications, and enable visually immersive experiences – remain in their infancy. In this proposal, we tackle the challenge of automatic generation of 3D content for virtual worlds. Such 3D generated content enables versatility, with flexible rendering from arbitrary viewpoints that…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.