16 citations · 32 across the 14 of their papers we have counts for
14 papers
RelightVid: Temporal-Consistent Diffusion Model for Video Relighting
Ye Fang, Zeyi Sun, Shangzhan Zhang +6
Diffusion models have demonstrated remarkable success in image generation and editing, with recent advancements enabling albedo-preserving image relighting. However, applying these…
GPT4Scene: Understand 3D Scenes from Videos with Vision-Language Models
Zhangyang Qi, Zhixiong Zhang, Ye Fang +2
In recent years, 2D Vision-Language Models (VLMs) have made significant strides in image-text understanding tasks. However, their performance in 3D spatial comprehension, which is…
FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models
Tong Wu, Yinghao Xu, Ryan Po +6
Recent advances in text-to-image generation have enabled the creation of high-quality images with diverse applications. However, accurately describing desired visual attributes can…
IDArb: Intrinsic Decomposition for Arbitrary Number of Input Views and Illuminations
Zhibing Li, Tong Wu, Jing Tan +3
Capturing geometric and material information from images remains a fundamental challenge in computer vision and graphics. Traditional optimization-based methods often require hours…
Tailor3D: Customized 3D Assets Editing and Generation with Dual-Side Images
Zhangyang Qi, Yunhan Yang, Mengchen Zhang +7
Recent advances in 3D AIGC have shown promise in directly creating 3D objects from text and images, offering significant cost savings in animation and product design. However, deta…
Prism: A Framework for Decoupling and Assessing the Capabilities of VLMs
Yuxuan Qiao, Haodong Duan, Xinyu Fang +6
Vision Language Models (VLMs) demonstrate remarkable proficiency in addressing a wide array of visual questions, which requires strong perception and reasoning faculties. Assessing…
Make-it-Real: Unleashing Large Multimodal Model for Painting 3D Objects with Realistic Materials
Ye Fang, Zeyi Sun, Tong Wu +4
Physically realistic materials are pivotal in augmenting the realism of 3D assets across various applications and lighting conditions. However, existing 3D assets and generative mo…
How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites
Zhe Chen, Weiyun Wang, Hao Tian +32
In this report, we introduce InternVL 1.5, an open-source multimodal large language model (MLLM) to bridge the capability gap between open-source and proprietary commercial models…
SepRep-Net: Multi-source Free Domain Adaptation via Model Separation And Reparameterization
Ying Jin, Jiaqi Wang, Dahua Lin
We consider multi-source free domain adaptation, the problem of adapting multiple existing models to a new domain without accessing the source data. Among existing approaches, meth…
Gemini vs GPT-4V: A Preliminary Comparison and Combination of Vision-Language Models Through Qualitative Cases
Zhangyang Qi, Ye Fang, Mengchen Zhang +6
The rapidly evolving sector of Multi-modal Large Language Models (MLLMs) is at the forefront of integrating linguistic and visual processing in artificial intelligence. This paper…
Alpha-CLIP: A CLIP Model Focusing on Wherever You Want
Zeyi Sun, Ye Fang, Tong Wu +6
Contrastive Language-Image Pre-training (CLIP) plays an essential role in extracting valuable content information from images across diverse tasks. It aligns textual and visual mod…
HyperDreamer: Hyper-Realistic 3D Content Generation and Editing from a Single Image
Tong Wu, Zhibing Li, Shuai Yang +5
3D content creation from a single image is a long-standing yet highly desirable task. Recent advances introduce 2D diffusion priors, yielding reasonable results. However, existing…
OneLLM: One Framework to Align All Modalities with Language
Jiaming Han, Kaixiong Gong, Yiyuan Zhang +6
Multimodal large language models (MLLMs) have gained significant attention due to their strong multimodal understanding capability. However, existing works rely heavily on modality…
GPT4Point: A Unified Framework for Point-Language Understanding and Generation
Zhangyang Qi, Ye Fang, Zeyi Sun +5
Multimodal Large Language Models (MLLMs) have excelled in 2D image-text comprehension and image generation, but their understanding of the 3D world is notably deficient, limiting p…