We introduce Cap3D, an automatic approach for generating descriptive text for
3D objects. This approach utilizes pretrained models from image captioning,
image-text alignment, and LLM to consolidate captions from multiple views of a
3D asset, completely side-stepping the time-consuming and costly process of
manual annotation. We apply Cap3D to the recently introduced large-scale 3D
dataset, Objaverse, resulting in 660k 3D-text pairs. Our evaluation, conducted
using 41k human annotations from the same dataset, demonstrates that Cap3D
surpasses human-authored descriptions in terms of quality, cost, and speed.
Through effective prompt engineering, Cap3D rivals human performance in
generating geometric descriptions on 17k collected annotations from the ABO
dataset. Finally, we finetune Text-to-3D models on Cap3D and human captions,
and show Cap3D outperforms; and benchmark the SOTA including Point-E, Shape-E,
and DreamFusion.

Cap3D 是一种自动生成三维物体描述的方法，利用图像字幕、图像文本对齐和 LLM 的预训练模型，从多个视角的 3D 资源中整合标题，既提高了效率又降低了成本，并在大规模 3D 数据集 Objaverse 上实现了 660k 3D 文本对。通过有效的提示工程，Cap3D 在 ABO 数据集的 17k 个收集的注释中产生几何描述并与人类表现不相上下。在 Cap3D 和人类字幕上调整 Text-to-3D 模型，并展示 Cap3D 优于 Point-E、Shape-E 和 DreamFusion，是一项值得关注的自动化方法。