<story><title>MeshGPT: Generating triangle meshes with decoder-only transformers</title><url>https://nihalsid.github.io/mesh-gpt/</url></story><parent_chain><item><author>shaileshm</author><text>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.&lt;p&gt;&amp;quot;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.&amp;quot;&lt;p&gt;This idea is simply beautiful and so obvious in hindsight.&lt;p&gt;&amp;quot;To define the tokens to generate, we consider a practical approach to represent a mesh M for autoregressive generation: a sequence of triangles.&amp;quot;&lt;p&gt;More from paper. Just so cool!</text></item></parent_chain><comment><author>legel</author><text>It&amp;#x27;s cool, it&amp;#x27;s also par for the field of 3D reconstruction today.  I wouldn&amp;#x27;t describe this paper as particularly innovative or exceptional.&lt;p&gt;What do I think is really compelling in this field (given that it&amp;#x27;s my profession)?&lt;p&gt;This has me star-struck lately -- 3D meshing from a single image, a very large 3D reconstruction model trained on millions of all kinds of 3D models... &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;yiconghong.me&amp;#x2F;LRM&amp;#x2F;&quot; rel=&quot;nofollow noreferrer&quot;&gt;https:&amp;#x2F;&amp;#x2F;yiconghong.me&amp;#x2F;LRM&amp;#x2F;&lt;/a&gt;</text></comment>
<story><title>MeshGPT: Generating triangle meshes with decoder-only transformers</title><url>https://nihalsid.github.io/mesh-gpt/</url></story><parent_chain><item><author>shaileshm</author><text>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.&lt;p&gt;&amp;quot;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.&amp;quot;&lt;p&gt;This idea is simply beautiful and so obvious in hindsight.&lt;p&gt;&amp;quot;To define the tokens to generate, we consider a practical approach to represent a mesh M for autoregressive generation: a sequence of triangles.&amp;quot;&lt;p&gt;More from paper. Just so cool!</text></item></parent_chain><comment><author>hedgehog</author><text>Another thing to note here is this looks to be around seven total days of training on at most 4 A100s. Not all really cutting edge work requires a data center sized cluster.</text></comment>