title: Shap-E: Generating Conditional 3D Implicit Functions
summary: We present Shap-E, a conditional generative model for 3D assets. Unlike
  recent work on 3D generative models which produce a single output
  representation, Shap-E directly generates the parameters of implicit functions
  that can be rendered as both textured meshes and neural radiance fields. We
  train Shap-E in two stages: first, we train an encoder that deterministically
  maps 3D assets into the parameters of an implicit function; second, we train a
  conditional diffusion model on outputs of the encoder. When trained on a large
  dataset of paired 3D and text data, our resulting models are capable of
  generating complex and diverse 3D assets in a matter of seconds. When compared
  to Point-E, an explicit generative model over point clouds, Shap-E converges
  faster and reaches comparable or better sample quality despite modeling a
  higher-dimensional, multi-representation output space. We release model
  weights, inference code, and samples at https://github.com/openai/shap-e.
paper_authors._id: 64545156548f22be59837e77
paper_authors.name: Heewoo Jun
paper_authors._id: 64545156548f22be59837e78
paper_authors.name: Alex Nichol
paper_title: Shap-E: Generating Conditional 3D Implicit Functions
paper_summary: We present Shap-E, a conditional generative model for 3D assets. Unlike
  recent work on 3D generative models which produce a single output
  representation, Shap-E directly generates the parameters of implicit functions
  that can be rendered as both textured meshes and neural radiance fields. We
  train Shap-E in two stages: first, we train an encoder that deterministically
  maps 3D assets into the parameters of an implicit function; second, we train a
  conditional diffusion model on outputs of the encoder. When trained on a large
  dataset of paired 3D and text data, our resulting models are capable of
  generating complex and diverse 3D assets in a matter of seconds. When compared
  to Point-E, an explicit generative model over point clouds, Shap-E converges
  faster and reaches comparable or better sample quality despite modeling a
  higher-dimensional, multi-representation output space. We release model
  weights, inference code, and samples at https://github.com/openai/shap-e.
paper_ai_keywords: generative model 3D assets implicit functions textured meshes neural radiance fields encoder conditional diffusion model Point-E point clouds explicit generative model