Modern 3D generation methods can rapidly create shapes from sparse or single views, but their outputs often lack geometric detail due to computational constraints. We present DetailGen3D, a generative approach specifically designed to enhance these generated 3D shapes. Our key insight is to model the coarse-to-fine transformation directly through data-dependent flows in latent space, avoiding the computational overhead of large-scale 3D generative models. We introduce a token matching strategy that ensures accurate spatial correspondence during refinement, enabling local detail synthesis while preserving global structure. By carefully designing our training data to match the characteristics of synthesized coarse shapes, our method can effectively enhance shapes produced by various 3D generation and reconstruction approaches, from single-view to sparse multi-view inputs. Extensive experiments demonstrate that DetailGen3D achieves high-fidelity geometric detail synthesis while maintaining efficiency in training.

本研究针对现代3D生成方法在几何细节上不足的问题，提出了一种名为DetailGen3D的生成方法，旨在提升生成3D形状的质量。我们的方法通过数据依赖流直接建模粗细转换，确保局部细节合成的准确性，并保持全局结构的完整性，实验表明，DetailGen3D在高保真几何细节合成与训练效率方面表现出色。

DetailGen3D：通过数据依赖流增强生成的3D几何模型