  Leveraging multi-view diffusion models as priors for 3D optimization have
alleviated the problem of 3D consistency, e.g., the Janus face problem or the
content drift problem, in zero-shot text-to-3D models. However, the 3D
geometric fidelity of the output remains an unresolved issue; albeit the
rendered 2D views are realistic, the underlying geometry may contain errors
such as unreasonable concavities. In this work, we propose CorrespondentDream,
an effective method to leverage annotation-free, cross-view correspondences
yielded from the diffusion U-Net to provide additional 3D prior to the NeRF
optimization process. We find that these correspondences are strongly
consistent with human perception, and by adopting it in our loss design, we are
able to produce NeRF models with geometries that are more coherent with common
sense, e.g., more smoothed object surface, yielding higher 3D fidelity. We
demonstrate the efficacy of our approach through various comparative
qualitative results and a solid user study.
