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Polygon meshes are an efficient representation of 3D geometry, and are ofcentral importance in computer graphics, robotics and games development.Existing learning-based approaches have avoided the challenges of working with3D meshes, instead using alternative object representations that are morecompatible with neural architectures and training approaches. We present anapproach which models the mesh directly, predicting mesh vertices and facessequentially using a Transformer-based architecture. Our model can condition ona range of inputs, including object classes, voxels, and images, and becausethe model is probabilistic it can produce samples that capture uncertainty inambiguous scenarios. We show that the model is capable of producinghigh-quality, usable meshes, and establish log-likelihood benchmarks for themesh-modelling task. We also evaluate the conditional models on surfacereconstruction metrics against alternative methods, and demonstrate competitiveperformance despite not training directly on this task.

PolyGen: An Autoregressive Generative Model of 3D Meshes