Consistent3D: Towards Consistent High-Fidelity Text-to-3D Generation with Deterministic Sampling Prior
Authors: Zike Wu, Pan Zhou, Xuanyu Yi, Xiaoding Yuan, Hanwang Zhang

Score distillation sampling (SDS) and its variants have greatly boosted the development of text-to-3D generation but are vulnerable to geometry collapse and poor textures yet. To solve this issue we first deeply analyze the SDS and find that its distillation sampling process indeed corresponds to the trajectory sampling of a stochastic differential equation (SDE): SDS samples along an SDE trajectory to yield a less noisy sample which then serves as a guidance to optimize a 3D model. However the randomness in SDE sampling often leads to a diverse and unpredictable sample which is not always less noisy and thus is not a consistently correct guidance explaining the vulnerability of SDS. Since for any SDE there always exists an ordinary differential equation (ODE) whose trajectory sampling can deterministically and consistently converge to the desired target point as the SDE we propose a novel and effective "Consistent3D" method that explores the ODE deterministic sampling prior for text-to-3D generation. Specifically at each training iteration given a rendered image by a 3D model we first estimate its desired 3D score function by a pre-trained 2D diffusion model and build an ODE for trajectory sampling. Next we design a consistency distillation sampling loss which samples along the ODE trajectory to generate two adjacent samples and uses the less noisy sample to guide another more noisy one for distilling the deterministic prior into the 3D model. Experimental results show the efficacy of our Consistent3D in generating high-fidelity and diverse 3D objects and large-scale scenes as shown in Fig. 1. The codes are available at https://github.com/sail-sg/Consistent3D.