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We present Large Language Model for Mixed Reality (LLMR), a framework for the real-time creation and modification of interactive Mixed Reality experiences using LLMs. LLMR leverages novel strategies to tackle difficult cases where ideal…

Human-Computer Interaction · Computer Science 2024-03-25 Fernanda De La Torre , Cathy Mengying Fang , Han Huang , Andrzej Banburski-Fahey , Judith Amores Fernandez , Jaron Lanier

Recently, 3D understanding has become popular to facilitate autonomous agents to perform further decisionmaking. However, existing 3D datasets and methods are often limited to specific tasks. On the other hand, recent progress in Large…

Computer Vision and Pattern Recognition · Computer Science 2023-12-19 Mingsheng Li , Xin Chen , Chi Zhang , Sijin Chen , Hongyuan Zhu , Fukun Yin , Gang Yu , Tao Chen

The remarkable potential of multi-modal large language models (MLLMs) in comprehending both vision and language information has been widely acknowledged. However, the scarcity of 3D scenes-language pairs in comparison to their 2D…

Computer Vision and Pattern Recognition · Computer Science 2024-01-17 Zeju Li , Chao Zhang , Xiaoyan Wang , Ruilong Ren , Yifan Xu , Ruifei Ma , Xiangde Liu

Large language models (LLMs) have recently achieved human-level performance on a range of professional and academic benchmarks. The accessibility of these models has lagged behind their performance. State-of-the-art LLMs require costly…

Computation and Language · Computer Science 2023-11-10 Yuvanesh Anand , Zach Nussbaum , Adam Treat , Aaron Miller , Richard Guo , Ben Schmidt , GPT4All Community , Brandon Duderstadt , Andriy Mulyar

The disruptive technology provided by large-scale pre-trained language models (LLMs) such as ChatGPT or GPT-4 has received significant attention in several application domains, often with an emphasis on high-level opportunities and…

Human-Computer Interaction · Computer Science 2023-06-27 Philippe J. Giabbanelli

The development of 3D Vision-Language Models (VLMs), crucial for applications in robotics, autonomous driving, and augmented reality, is severely constrained by the scarcity of paired 3D-text data. Existing methods rely solely on next-token…

Computer Vision and Pattern Recognition · Computer Science 2026-03-03 Yuanhao Su , Shaofeng Zhang , Xiaosong Jia , Qi Fan

With the growing demands of AI-generated content (AIGC), the need for high-quality, diverse, and scalable data has become increasingly crucial. However, collecting large-scale real-world data remains costly and time-consuming, hindering the…

Computer Vision and Pattern Recognition · Computer Science 2025-08-08 Kunyu Feng , Yue Ma , Xinhua Zhang , Boshi Liu , Yikuang Yuluo , Yinhan Zhang , Runtao Liu , Hongyu Liu , Zhiyuan Qin , Shanhui Mo , Qifeng Chen , Zeyu Wang

Multimodal Large Language Models (MLLMs) mimic human perception and reasoning system by integrating powerful Large Language Models (LLMs) with various modality encoders (e.g., vision, audio), positioning LLMs as the "brain" and various…

Computer Vision and Pattern Recognition · Computer Science 2024-08-29 Jiaxing Huang , Jingyi Zhang

Reasoning segmentation aims to segment target objects in complex scenes based on human intent and spatial reasoning. While recent multimodal large language models (MLLMs) have demonstrated impressive 2D image reasoning segmentation,…

Computer Vision and Pattern Recognition · Computer Science 2025-11-11 Jiaxin Huang , Runnan Chen , Ziwen Li , Zhengqing Gao , Xiao He , Yandong Guo , Mingming Gong , Tongliang Liu

Multimodal large language models (MLLMs), such as GPT-4o, Gemini, LLaVA, and Flamingo, have made significant progress in integrating visual and textual modalities, excelling in tasks like visual question answering (VQA), image captioning,…

Computer Vision and Pattern Recognition · Computer Science 2024-12-31 Junxiao Xue , Quan Deng , Fei Yu , Yanhao Wang , Jun Wang , Yuehua Li

The recent surge of Multimodal Large Language Models (MLLMs) has fundamentally reshaped the landscape of AI research and industry, shedding light on a promising path toward the next AI milestone. However, significant challenges remain…

Generative Pre-trained Transformer 4 (GPT-4) is the fourth-generation language model in the GPT series, developed by OpenAI, which promises significant advancements in the field of natural language processing (NLP). In this research…

Computation and Language · Computer Science 2023-05-08 Jawid Ahmad Baktash , Mursal Dawodi

Attaining a high degree of user controllability in visual generation often requires intricate, fine-grained inputs like layouts. However, such inputs impose a substantial burden on users when compared to simple text inputs. To address the…

Computer Vision and Pattern Recognition · Computer Science 2023-10-31 Weixi Feng , Wanrong Zhu , Tsu-jui Fu , Varun Jampani , Arjun Akula , Xuehai He , Sugato Basu , Xin Eric Wang , William Yang Wang

Large language models (LLMs) have demonstrated remarkable language abilities. GPT-4, based on advanced LLMs, exhibits extraordinary multimodal capabilities beyond previous visual language models. We attribute this to the use of more…

Computation and Language · Computer Science 2023-05-23 Feilong Chen , Minglun Han , Haozhi Zhao , Qingyang Zhang , Jing Shi , Shuang Xu , Bo Xu

This work explores expanding the capabilities of large language models (LLMs) pretrained on text to generate 3D meshes within a unified model. This offers key advantages of (1) leveraging spatial knowledge already embedded in LLMs, derived…

Machine Learning · Computer Science 2024-11-15 Zhengyi Wang , Jonathan Lorraine , Yikai Wang , Hang Su , Jun Zhu , Sanja Fidler , Xiaohui Zeng

We introduce Uni4D, a unified framework for large scale open vocabulary 3D retrieval and controlled 4D generation based on structured three level alignment across text, 3D models, and image modalities. Built upon the Align3D 130 dataset,…

Computer Vision and Pattern Recognition · Computer Science 2026-01-30 Philip Xu

Multi-modal large language models (MLLMs) have shown incredible capabilities in a variety of 2D vision and language tasks. We extend MLLMs' perceptual capabilities to ground and reason about images in 3-dimensional space. To that end, we…

Computer Vision and Pattern Recognition · Computer Science 2024-05-07 Jang Hyun Cho , Boris Ivanovic , Yulong Cao , Edward Schmerling , Yue Wang , Xinshuo Weng , Boyi Li , Yurong You , Philipp Krähenbühl , Yan Wang , Marco Pavone

Engineering design is undergoing a transformative shift with the advent of AI, marking a new era in how we approach product, system, and service planning. Large language models have demonstrated impressive capabilities in enabling this…

Artificial Intelligence · Computer Science 2024-12-10 Cyril Picard , Kristen M. Edwards , Anna C. Doris , Brandon Man , Giorgio Giannone , Md Ferdous Alam , Faez Ahmed

Geometric understanding - including depth and height perception - is fundamental to intelligence and crucial for navigating our environment. Despite the impressive capabilities of large Vision Language Models (VLMs), it remains unclear how…

Computer Vision and Pattern Recognition · Computer Science 2025-04-28 Shehreen Azad , Yash Jain , Rishit Garg , Yogesh S Rawat , Vibhav Vineet

Despite remarkable progress in Multimodal Large Language Models (MLLMs), these models still struggle with fine-grained understanding tasks. In this work, we propose Procedurally Generated Tasks (PGT), a simple data-driven framework that…

Computer Vision and Pattern Recognition · Computer Science 2026-05-25 Rim Assouel , Amir Bar , Michal Drozdzal , Adriana Romero-Soriano