DreamPolish：基于渐进式几何生成的域分数蒸馏
DreamPolish: Domain Score Distillation With Progressive Geometry Generation
AI总结：
本研究提出文本到3D生成模型DreamPolish，通过多神经表征+法线估计的渐进几何生成优化表面伪影，结合域分数蒸馏提升纹理真实感与一致性，效果优于现有SOTA方法。
摘要：
我们提出了DreamPolish，这是一款文本到3D生成模型，擅长生成精细化几何结构与高质量纹理。在几何构建阶段，我们的方法利用多种神经表征提升合成过程的稳定性。我们没有仅依赖新采样视图中的视图条件扩散先验——这种先验常会导致几何表面出现非预期伪影，而是引入了一个额外的法线估计器，以不同视场的视角为条件来打磨几何细节。我们提出添加一个仅需少量训练步骤的表面打磨阶段，可有效修正因前序阶段引导有限产生的伪影，生成几何结构更理想的3D对象。使用预训练文本到图像模型生成纹理的核心问题，是在这些模型的庞大潜在分布中找到包含真实感且一致渲染结果的合适域。在纹理生成阶段，我们提出了一种新颖的分数蒸馏目标，即域分数蒸馏（DSD），引导神经表征向该域靠拢。我们从文本条件图像生成任务中的无分类器引导（CFG）中获得灵感，证明CFG和变分分布引导在梯度引导中代表不同维度，且两者都是提升纹理质量不可或缺的域。大量实验表明，我们提出的模型能够生成表面打磨精细、纹理具有真实感的3D资产，性能优于现有最先进方法。
英文摘要：
We introduce DreamPolish, a text-to-3D generation model that excels in producing refined geometry and high-quality textures. In the geometry construction phase, our approach leverages multiple neural representations to enhance the stability of the synthesis process. Instead of relying solely on a view-conditioned diffusion prior in the novel sampled views, which often leads to undesired artifacts in the geometric surface, we incorporate an additional normal estimator to polish the geometry details, conditioned on viewpoints with varying field-of-views. We propose to add a surface polishing stage with only a few training steps, which can effectively refine the artifacts attributed to limited guidance from previous stages and produce 3D objects with more desirable geometry. The key topic of texture generation using pretrained text-to-image models is to find a suitable domain in the vast latent distribution of these models that contains photorealistic and consistent renderings. In the texture generation phase, we introduce a novel score distillation objective, namely domain score distillation (DSD), to guide neural representations toward such a domain. We draw inspiration from the classifier-free guidance (CFG) in textconditioned image generation tasks and show that CFG and variational distribution guidance represent distinct aspects in gradient guidance and are both imperative domains for the enhancement of texture quality. Extensive experiments show our proposed model can produce 3D assets with polished surfaces and photorealistic textures, outperforming existing state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
