GALA: Geometry-Aware Local Adaptive Grids for Detailed 3D Generation

Based on our Octree Forests setting of GALA, we are able to naturally construct the generation process in a cascaded fashion. As shown below: (1) Root Voxel Diffusion of vector set \( X_o = \{\mathbf{x_o}\in\mathbb{R}^4|(\mathbf{p}, s)\} \); (2) Grid Config Diffusion of vector set \( X_{\bar{V}} = \{\mathbf{x}_{\bar{V}}\in\mathbb{R}^10|(\mathbf{p}_g, \mathbf{q}, \mathbf{s}_g)\} \); (3) and Grid Value Regression \( X_V = \{ \mathbf{x}_V \in \mathbb{R}^{m^3} \} \). For more on the symbols used here, please refer to Figure. 3 of the paper. Qualitative comparisons with other baselines are also shown below.

As shown above, given coarse and partial inputs (the root voxels in orange), we firstly autocomplete the rest root voxles (blue) and then generate the geometries. This coarse-to-fine generation pipeline has the potential of openning up a possible way of interactive 3D creation in the future.

First carving the mesh and then adding textures is a standard work- flow in 3D asset creation within the industry, which allows flexibility of creating various textures while reusing the fine meshes already crafted. Many deep learning works follow this strategy. Here we use Easi-Tex to texture the generated meshes guided by reference images, which in turn indicates the high quality of our generated meshes.