View Selection for 3D Captioning via Diffusion Ranking
Tiange Luo Justin Johnson Honglak Lee
Scalable annotation approaches are crucial for constructing extensive 3D-text
datasets, facilitating a broader range of applications. However, existing
methods sometimes lead to the generation of hallucinated captions, compromising
caption quality. This paper explores the issue of hallucination in 3D object
captioning, with a focus on Cap3D method, which renders 3D objects into 2D
views for captioning using pre-trained models. We pinpoint a major challenge:
certain rendered views of 3D objects are atypical, deviating from the training
data of standard image captioning models and causing hallucinations. To tackle
this, we present DiffuRank, a method that leverages a pre-trained text-to-3D
model to assess the alignment between 3D objects and their 2D rendered views,
where the view with high alignment closely represent the object's
characteristics. By ranking all rendered views and feeding the top-ranked ones
into GPT4-Vision, we enhance the accuracy and detail of captions, enabling the
correction of 200k captions in the Cap3D dataset and extending it to 1 million
captions across Objaverse and Objaverse-XL datasets. Additionally, we showcase
the adaptability of DiffuRank by applying it to pre-trained text-to-image
models for a Visual Question Answering task, where it outperforms the CLIP
model.