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IITM & UT Austin’s Generalizable NeRF Transformer Demonstrates Transformers’ Capabilities for Graphical Rendering
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In the new paper Is Attention All NeRF Needs?, a research team from the Indian Institute of Technology Madras and the University of Texas at Austin proposes Generalizable NeRF Transformer (GNT), a pure and universal transformer-based architecture for efficient on-the-fly reconstruction of NeRFs. The work demonstrates that a pure attention mechanism suffices for learning a physically-grounded rendering process.
The post IITM & UT Austin’s Generalizable NeRF Transformer Demonstrates Transformers’ Capabilities for Graphical Rendering first appeared on Synced.
ai artificial intelligence austin computer vision & graphics deep-neural-networks machine learning machine learning & data science ml nerf neural radiance fields research technology transformer transformers