DreamDPO：通过直接偏好优化将文本到3D生成与人类偏好对齐
DreamDPO: Aligning Text-to-3D Generation with Human Preferences via Direct Preference Optimization
AI总结：
本文提出DreamDPO框架，通过直接偏好优化将人类偏好集成到文本到3D生成中，利用成对比较和偏好驱动损失函数实现更高质量、更可控的3D内容生成。
摘要：
文本到3D生成能够根据文本描述自动创建3D内容，在各个领域具有变革性潜力。然而，现有方法往往难以使生成内容与人类偏好对齐，限制了其适用性和灵活性。为了解决这些局限性，本文提出了DreamDPO，一个基于优化的框架，通过直接偏好优化将人类偏好集成到3D生成过程中。具体而言，DreamDPO首先构建成对示例，然后使用奖励模型或大型多模态模型比较它们与人类偏好的一致性，最后利用偏好驱动的损失函数优化3D表示。通过利用成对比较来反映偏好，DreamDPO减少了对精确逐点质量评估的依赖，同时通过偏好引导的优化实现了细粒度可控性。实验表明，DreamDPO取得了具有竞争力的结果，并且与现有方法相比，能够生成更高质量、更可控的3D内容。代码和模型将开源。
英文摘要：
Text-to-3D generation automates 3D content creation from textual descriptions, which offers transformative potential across various fields. However, existing methods often struggle to align generated content with human preferences, limiting their applicability and flexibility. To address these limitations, in this paper, we propose DreamDPO, an optimization-based framework that integrates human preferences into the 3D generation process, through direct preference optimization. Practically, DreamDPO first constructs pairwise examples, then compare their alignment with human preferences using reward or large multimodal models, and lastly optimizes the 3D representation with a preference-driven loss function. By leveraging pairwise comparison to reflect preferences, DreamDPO reduces reliance on precise pointwise quality evaluations while enabling fine-grained controllability through preference-guided optimization. Experiments demonstrate that DreamDPO achieves competitive results, and provides higher-quality and more controllable 3D content compared to existing methods. The code and models will be open-sourced.
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。
