SOURCE-LINKED INTELLIGENCE

Empowering Neural Rendering Methods with Physically-Based Capabilities

CORDIS · observation · Publication date unknown

Empowering Neural Rendering Methods with Physically-Based Capabilities 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 limitat

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recordType

award

status

SIGNED

region

EU

value

2488029

unit

EUR