  We introduce a new generative approach for synthesizing 3D geometry and
images from single-view collections. Most existing approaches predict
volumetric density to render multi-view consistent images. By employing
volumetric rendering using neural radiance fields, they inherit a key
limitation: the generated geometry is noisy and unconstrained, limiting the
quality and utility of the output meshes. To address this issue, we propose
GeoGen, a new SDF-based 3D generative model trained in an end-to-end manner.
Initially, we reinterpret the volumetric density as a Signed Distance Function
(SDF). This allows us to introduce useful priors to generate valid meshes.
However, those priors prevent the generative model from learning details,
limiting the applicability of the method to real-world scenarios. To alleviate
that problem, we make the transformation learnable and constrain the rendered
depth map to be consistent with the zero-level set of the SDF. Through the lens
of adversarial training, we encourage the network to produce higher fidelity
details on the output meshes. For evaluation, we introduce a synthetic dataset
of human avatars captured from 360-degree camera angles, to overcome the
challenges presented by real-world datasets, which often lack 3D consistency
and do not cover all camera angles. Our experiments on multiple datasets show
that GeoGen produces visually and quantitatively better geometry than the
previous generative models based on neural radiance fields.
