DreamDPO: Aligning Text-to-3D Generation with Human Preferences via
  Direct Preference Optimization
Authors: name: Zhenglin Zhou, name: Xiaobo Xia, name: Fan Ma, name: Hehe Fan, name: Yi Yang, name: Tat-Seng Chua

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.