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Controllable generation of 3D assets is important for many practicalapplications like content creation in movies, games and engineering, as well asin AR/VR. Recently, diffusion models have shown remarkable results ingeneration quality of 3D objects. However, none of the existing models enabledisentangled generation to control the shape and appearance separately. For thefirst time, we present a suitable representation for 3D diffusion models toenable such disentanglement by introducing a hybrid point cloud and neuralradiance field approach. We model a diffusion process over point positionsjointly with a high-dimensional feature space for a local density and radiancedecoder. While the point positions represent the coarse shape of the object,the point features allow modeling the geometry and appearance details. Thisdisentanglement enables us to sample both independently and therefore tocontrol both separately. Our approach sets a new state of the art in generationcompared to previous disentanglement-capable methods by reduced FID scores of30-90% and is on-par with other non disentanglement-capable state-of-the artmethods.

Neural Point Cloud Diffusion for Disentangled 3D Shape and Appearance Generation