LCGen: Mining in Low-Certainty Generation for View-consistent Text-to-3D

Zeng Tao, Tong Yang, Junxiong Lin, Xinji Mai, Haoran Wang +4 more
2/3/2026

No confident code implementation found for this paper. 1 possible match can be shown below.

Abstract

The Janus Problem is a common issue in SDS-based text-to-3D methods. Due to view encoding approach and 2D diffusion prior guidance, the 3D representation model tends to learn content with higher certainty from each perspective, leading to view inconsistency. In this work, we first model and analyze the problem, visualizing the specific causes of the Janus Problem, which are associated with discrete view encoding and shared priors in 2D lifting. Based on this, we further propose the LCGen method, which guides text-to-3D to obtain different priors with different certainty from various viewpoints,

DOISemantic Scholar

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Cite this paper

@article{tao2026lcgen,
  title  = {LCGen: Mining in Low-Certainty Generation for View-consistent Text-to-3D},
  author = {Zeng Tao and Tong Yang and Junxiong Lin and Xinji Mai and Haoran Wang and Beining Wang and Enyu Zhou and Yan Wang and Wenqiang Zhang},
  year   = {2026},
  doi    = {10.52202/079017-0641},
  url    = {https://doi.org/10.52202/079017-0641},
  journal = {NEURIPS 2024 2024}
}

Discussion