Taming Mode Collapse in Score Distillation for Text-to-3D Generation
Authors: name: Peihao Wang, name: Dejia Xu, name: Zhiwen Fan, name: Dilin Wang, name: Sreyas Mohan, name: Forrest Iandola, name: Rakesh Ranjan, name: Yilei Li, name: Qiang Liu, name: Zhangyang Wang, name: Vikas Chandra

Despite the remarkable performance of score distillation in text-to-3D
generation, such techniques notoriously suffer from view inconsistency issues,
also known as "Janus" artifact, where the generated objects fake each view with
multiple front faces. Although empirically effective methods have approached
this problem via score debiasing or prompt engineering, a more rigorous
perspective to explain and tackle this problem remains elusive. In this paper,
we reveal that the existing score distillation-based text-to-3D generation
frameworks degenerate to maximal likelihood seeking on each view independently
and thus suffer from the mode collapse problem, manifesting as the Janus
artifact in practice. To tame mode collapse, we improve score distillation by
re-establishing in entropy term in the corresponding variational objective,
which is applied to the distribution of rendered images. Maximizing the entropy
encourages diversity among different views in generated 3D assets, thereby
mitigating the Janus problem. Based on this new objective, we derive a new
update rule for 3D score distillation, dubbed Entropic Score Distillation
(ESD). We theoretically reveal that ESD can be simplified and implemented by
just adopting the classifier-free guidance trick upon variational score
distillation. Although embarrassingly straightforward, our extensive
experiments successfully demonstrate that ESD can be an effective treatment for
Janus artifacts in score distillation.