While text-to-3D generation has attracted growing interest, existing methods often struggle to produce 3D assets that align well with human preferences. Current preference alignment techniques for 3D content typically rely on hardly-collected preference-paired multi-view 2D images to train 2D reward models, when then guide 3D generation -- leading to geometric artifacts due to their inherent 2D bias. To address these limitations, we construct 3D-MeshPref, the first large-scale unpaired 3D preference dataset, featuring diverse 3D meshes annotated by a large language model and refined by human evaluators. We then develop RewardCS, the first reward model trained directly on unpaired 3D-MeshPref data using a novel Cauchy-Schwarz divergence objective, enabling effective learning of human-aligned 3D geometric preferences without requiring paired comparisons. Building on this, we propose DreamCS, a unified framework that integrates RewardCS into text-to-3D pipelines -- enhancing both implicit and explicit 3D generation with human preference feedback. Extensive experiments show DreamCS outperforms prior methods, producing 3D assets that are both geometrically faithful and human-preferred. Code and models will be released publicly.

翻译：文本到三维生成技术日益受到关注，但现有方法常难以生成符合人类偏好的三维资产。当前三维内容的偏好对齐技术通常依赖难以收集的成对偏好多视角二维图像来训练二维奖励模型，再通过该模型指导三维生成——这导致因固有二维偏差而产生的几何伪影。为突破这些局限，我们构建了首个大规模无配对三维偏好数据集3D-MeshPref，其中包含经大语言模型标注并由人工评估者优化的多样化三维网格。进而开发RewardCS——首个基于无配对3D-MeshPref数据，采用新颖的柯西-施瓦茨散度目标直接训练的奖励模型，无需成对比较即可有效学习人类对齐的三维几何偏好。在此基础上提出统一框架DreamCS，将RewardCS集成至文本到三维生成管线中，通过人类偏好反馈增强隐式和显式三维生成。大量实验表明，DreamCS优于先前方法，能生成在几何保真度和人类偏好方面均表现卓越的三维资产。代码与模型将公开发布。