This is what a truly revolutionary idea looks like. There are so many details in the paper. Also, we know that transformers can scale. Pretty sure this idea will be used by a lot of companies to train the general 3D asset creation pipeline. This is just too great.
"We first learn a vocabulary of latent quantized embeddings, using graph convolutions, which inform these embeddings of the local mesh geometry and topology. These embeddings are sequenced and decoded into triangles by a decoder, ensuring that they can effectively reconstruct the mesh."
This idea is simply beautiful and so obvious in hindsight.
"To define the tokens to generate, we consider a practical approach to represent a mesh M for autoregressive generation: a sequence of triangles."
"We first learn a vocabulary of latent quantized embeddings, using graph convolutions, which inform these embeddings of the local mesh geometry and topology. These embeddings are sequenced and decoded into triangles by a decoder, ensuring that they can effectively reconstruct the mesh."
This idea is simply beautiful and so obvious in hindsight.
"To define the tokens to generate, we consider a practical approach to represent a mesh M for autoregressive generation: a sequence of triangles."
More from paper. Just so cool!