Scalable 3D Captioning with Pretrained Models
Authors: name: Tiange Luo, name: Chris Rockwell, name: Honglak Lee, name: Justin Johnson

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.