3 citations · 3 across the 1 of their papers we have counts for
5 papers
3DAxisPrompt: Promoting the 3D Grounding and Reasoning in GPT-4o
Dingning Liu, Cheng Wang, Peng Gao +4
Multimodal Large Language Models (MLLMs) exhibit impressive capabilities across a variety of tasks, especially when equipped with carefully designed visual prompts. However, existi…
TAR3D: Creating High-Quality 3D Assets via Next-Part Prediction
Xuying Zhang, Yutong Liu, Yangguang Li +8
We present TAR3D, a novel framework that consists of a 3D-aware Vector Quantized-Variational AutoEncoder (VQ-VAE) and a Generative Pre-trained Transformer (GPT) to generate high-qu…
Uni3D-LLM: Unifying Point Cloud Perception, Generation and Editing with Large Language Models
Dingning Liu, Xiaoshui Huang, Yuenan Hou +5
In this paper, we introduce Uni3D-LLM, a unified framework that leverages a Large Language Model (LLM) to integrate tasks of 3D perception, generation, and editing within point clo…
A Challenger to GPT-4V? Early Explorations of Gemini in Visual Expertise
Chaoyou Fu, Renrui Zhang, Zihan Wang +15
The surge of interest towards Multi-modal Large Language Models (MLLMs), e.g., GPT-4V(ision) from OpenAI, has marked a significant trend in both academia and industry. They endow L…
3DAxiesPrompts: Unleashing the 3D Spatial Task Capabilities of GPT-4V
Dingning Liu, Xiaomeng Dong, Renrui Zhang +5
In this work, we present a new visual prompting method called 3DAxiesPrompts (3DAP) to unleash the capabilities of GPT-4V in performing 3D spatial tasks. Our investigation reveals…