Important

You are viewing the NeMo 2.0 documentation. This release introduces significant changes to the API and a new library, NeMo Run. We are currently porting all features from NeMo 1.0 to 2.0. For documentation on previous versions or features not yet available in 2.0, please refer to the NeMo 24.07 documentation.

DreamFusion-DMTet

NeRF (Neural Radiance Fields) models integrate geometry and appearance through volume rendering. As a result, using NeRF for 3D modeling can be less effective when it comes to capturing both the intricate details of a surface as well as its material and texture.

DMTet fine-tunning disentangles the learning of geometry and appearance models, such that both a fine surface and a rich material/texture can be generated. To enable such a disentangled learning, a hybrid scene representation of DMTet is used.

The DMTet model maintains a deformable tetrahedral grid that encodes a discretized signed distance function and a differentiable marching tetrahedra layer that converts the implicit signed distance representation to the explicit surface mesh representation.