SOURCE-LINKED INTELLIGENCE

Learning to synthesize interactive 3D models

CORDIS · observation · Publication date unknown

representations of environments (i.e., 3D models) often requires excessive amount of manual effort and time even for trained 3D artists. Over the recent years, there have been remarkable advances in deep learning methods that attempt to reconstruct 3D models from real-world data captured in images or scans. However, we are still far from automatically producing 3D models usable in interactive 3D environments and simulations i.e., the resulting reconstructed 3D models lack controllers and metadata related to their articulation structure, possible motions, and interaction with other objects or agents. Automating the synthesis of interactive 3D models is crucial for several applications, such as (a) virtual and mixed reality environments where objects and characters are not static, but instead move and interact with each other, (b) automating animation pipelines, (c) training robots for object interaction in simulated environments, (d) 3D printing of functional objects, (e) digital entertainment. In this project, we will answer the question: ""how can we automate the generation of inte

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recordType

award

status

SIGNED

region

EU

value

2000000

unit

EUR