The field of visual computing is rapidly advancing due to the emergence of generative
artificial intelligence (AI), which unlocks unprecedented capabilities for the generation …

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 …

Recent advancements in multimodal pre-training have shown promising efficacy in 3D
representation learning by aligning multimodal features across 3D shapes their 2D …

We introduce OpenShape, a method for learning multi-modal joint representations of text,
image, and point clouds. We adopt the commonly used multi-modal contrastive learning …

In the field of visual scene understanding, deep neural networks have made impressive
advancements in various core tasks like segmentation, tracking, and detection. However …

Large vision-language models have achieved outstanding performance, but their size and
computational requirements make their deployment on resource-constrained devices and …

We present a novel alignment-before-generation approach to tackle the challenging task of
generating general 3D shapes based on 2D images or texts. Directly learning a conditional …

We introduce the task of localizing a flexible number of objects in real-world 3D scenes
using natural language descriptions. Existing 3D visual grounding tasks focus on localizing …

Large language models (LLMs) based on the generative pre-training transformer (GPT)
have demonstrated remarkable effectiveness across a diverse range of downstream tasks …

Recent developments in foundation models, like Large Language Models (LLMs) and Vision-
Language Models (VLMs), trained on extensive data, facilitate flexible application across …