Shap-E: Generating Conditional 3D Implicit Functions
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.
64545156548f22be59837e77
Heewoo Jun
64545156548f22be59837e78
Alex Nichol
Shap-E: Generating Conditional 3D Implicit Functions
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.
Shap-E, a conditional generative model, generates 3D assets by training an encoder and a conditional diffusion model, achieving faster convergence and high-quality samples compared to explicit models.
conditional generative model implicit functions textured meshes neural radiance fields encoder conditional diffusion model 3D assets Point-E explicit generative model point clouds