Text-to-3D generation aims to create 3D assets from text-to-image diffusion
models. However, existing methods face an inherent bottleneck in generation
quality because the widely-used objectives such as Score Distillation Sampling
(SDS) inappropriately omit U-Net jacobians for swift generation, leading to
significant bias compared to the "true" gradient obtained by full denoising
sampling. This bias brings inconsistent updating direction, resulting in
implausible 3D generation e.g., color deviation, Janus problem, and
semantically inconsistent details). In this work, we propose Pose-dependent
Consistency Distillation Sampling (PCDS), a novel yet efficient objective for
diffusion-based 3D generation tasks. Specifically, PCDS builds the
pose-dependent consistency function within diffusion trajectories, allowing to
approximate true gradients through minimal sampling steps (1-3). Compared to
SDS, PCDS can acquire a more accurate updating direction with the same sampling
time (1 sampling step), while enabling few-step (2-3) sampling to trade compute
for higher generation quality. For efficient generation, we propose a
coarse-to-fine optimization strategy, which first utilizes 1-step PCDS to
create the basic structure of 3D objects, and then gradually increases PCDS
steps to generate fine-grained details. Extensive experiments demonstrate that
our approach outperforms the state-of-the-art in generation quality and
training efficiency, conspicuously alleviating the implausible 3D generation
issues caused by the deviated updating direction. Moreover, it can be simply
applied to many 3D generative applications to yield impressive 3D assets,
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提出了一种基于姿态相关性蒸馏采样（PCDS）的扩散式三维生成任务的新目标，其通过最小采样步骤（1-3）建立了在扩散轨迹内的姿态相关性函数，以近似真实梯度，并通过粗到细的优化策略实现高质量的三维物体生成。