Project description
AI-driven 3D with physics-based control
3D content creation involves complex processes, requiring specialised artists to create the 3D models and expensive algorithms to render the final images. Such solutions are used in industries such as digital twins, architecture, virtual reality, special effects and many others. Traditional 3D content creation uses physically-based rendering, which simulates light transport according to the laws of physics, ensuring highly accurate and realistic results – but typically at a high computational cost. By contrast, neural rendering is a powerful recent solution that uses deep learning to generate visuals based on learned representations but lacks physical accuracy and control. The ERC-funded NERPHYS project proposes to introduce novel representations and neural rendering algorithms allowing computationally efficient creation of 3D content enhanced with physics-based control.
Objective
While long restricted to an elite of expert digital artists, 3D content creation has recently been greatly simplified by deep learning. Neural representations of 3D objects have revolutionized real-world capture from photos, while generative models are starting to enable 3D object synthesis from text prompts. These methods use differentiable neural rendering that allows efficient optimization of the powerful and expressive ``soft'' neural representations, but ignores physically-based principles, and thus has no guarantees on accuracy, severely limiting the utility of the resulting content.
Differentiable physically-based rendering on the other hand can produce 3D assets with physics-based parameters, but depends on rigid traditional ``hard'' graphics representations required for light-transport computation, that make optimization much harder and is also costly, limiting applicability.
In NERPHYS we will combine the strengths of both neural and physically-based rendering, lifting their respective limitations by introducing polymorphic 3D representations, i.e. capable of morphing between different states to accommodate both efficient gradient-based optimization and physically-based light transport. By augmenting these representations with corresponding polymorphic differentiable renderers, our methodology will unleash the potential of neural rendering to produce physically-based 3D assets with guarantees on accuracy.
NERPHYS will have ground-breaking impact on 3D content creation, moving beyond today's simplistic plausible imagery, to full physically-based rendering with guarantees on error, enabling the use of powerful neural rendering methods in any application requiring accuracy. Our polymorphic approach will fundamentally change how we reason about scene representations for geometry and appearance,
while our rendering algorithms will provide a new methodology for image synthesis, e.g. for training data generation or visual effects.
Fields of science (EuroSciVoc)
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
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Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
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Call for proposal
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(opens in new window) ERC-2023-ADG
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78153 Le Chesnay Cedex
France
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