  Diffusion-based image generators can now produce high-quality and diverse
samples, but their success has yet to fully translate to 3D generation:
existing diffusion methods can either generate low-resolution but 3D consistent
outputs, or detailed 2D views of 3D objects but with potential structural
defects and lacking view consistency or realism. We present HoloFusion, a
method that combines the best of these approaches to produce high-fidelity,
plausible, and diverse 3D samples while learning from a collection of
multi-view 2D images only. The method first generates coarse 3D samples using a
variant of the recently proposed HoloDiffusion generator. Then, it
independently renders and upsamples a large number of views of the coarse 3D
model, super-resolves them to add detail, and distills those into a single,
high-fidelity implicit 3D representation, which also ensures view consistency
of the final renders. The super-resolution network is trained as an integral
part of HoloFusion, end-to-end, and the final distillation uses a new sampling
scheme to capture the space of super-resolved signals. We compare our method
against existing baselines, including DreamFusion, Get3D, EG3D, and
HoloDiffusion, and achieve, to the best of our knowledge, the most realistic
results on the challenging CO3Dv2 dataset.
