SOURCE-LINKED INTELLIGENCE

Learning to Create Virtual Worlds

CORDIS · observation · Publication date unknown

s, movies, AR/VR scenarios, CAD modeling, architectural & industrial design, and medical applications. We believe that the key towards automated, high-fidelity content creation lies in developing new machine learning techniques to transform 3D content generation. (A) We will develop 3D Generative Models that output 3D polygon meshes, along with their surface textures and material properties, highlighting generation of 3D content that can be directly consumed by modern graphics pipelines. (B) To train our 3D generative models to reflect the complexity and diversity of real data, we will devise methods for Supervision from Images and Videos. The key challenge here is that such collections of images and videos are by nature incomplete projections of the underlying 3D world, thus requiring learning paradigms that generalize across partial instances. (C) We will research techniques that provide Control and Editability through Conditional Generation. In particular, we will focus on conditional input from both novice (e.g., text-based editing) and expert (e.g., based on existing authorin

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recordType

award

status

SIGNED

region

EU

value

2750000

unit

EUR