Advancing Text-to-3D Generation with Linearized Lookahead Variational Score Distillation

TL;DR

This paper introduces $L^2$-VSD, a linearized lookahead variational score distillation method that improves text-to-3D generation quality by addressing convergence issues in existing score distillation techniques.

Contribution

It proposes a linearized lookahead approach to enhance variational score distillation, leading to more stable training and better 3D generation results.

Findings

$L^2$-VSD outperforms prior methods in quality.

The method is efficient with existing autodiff tools.

It can be integrated into various VSD-based frameworks.

Abstract

Text-to-3D generation based on score distillation of pre-trained 2D diffusion models has gained increasing interest, with variational score distillation (VSD) as a remarkable example. VSD proves that vanilla score distillation can be improved by introducing an extra score-based model, which characterizes the distribution of images rendered from 3D models, to correct the distillation gradient. Despite the theoretical foundations, VSD, in practice, is likely to suffer from slow and sometimes ill-posed convergence. In this paper, we perform an in-depth investigation of the interplay between the introduced score model and the 3D model, and find that there exists a mismatching problem between LoRA and 3D distributions in practical implementation. We can simply adjust their optimization order to improve the generation quality. By doing so, the score model looks ahead to the current 3D state and hence yields more reasonable corrections. Nevertheless, naive lookahead VSD may suffer from unstable training in practice due to the potential over-fitting. To address this, we propose to use a linearized variant of the model for score distillation, giving rise to the Linearized Lookahead Variational Score Distillation ($L^2$-VSD). $L^2$-VSD can be realized efficiently with forward-mode autodiff functionalities of existing deep learning libraries. Extensive experiments validate the efficacy of $L^2$-VSD, revealing its clear superiority over prior score distillation-based methods. We also show that our method can be seamlessly incorporated into any other VSD-based text-to-3D framework.