Consistent3D: Towards Consistent High-Fidelity Text-to-3D Generation
  with Deterministic Sampling Prior
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.