172 citations · 199 across the 16 of their papers we have counts for
16 papers · 1 filter
PhysRAG: Enhancing Physics-Awareness in Video Generation via Retrieval-Augmented Generation
Kexu Cheng, Zicheng Liu, Mingju Gao +2
Developing physically aware video generation models remains a significant challenge due to the difficulty in capturing diverse physical phenomena, such as thermal dynamics, mechani…
Conditional Text-to-Image Generation with Reference Guidance
Taewook Kim, Ze Wang, Zhengyuan Yang +4
Text-to-image diffusion models have demonstrated tremendous success in synthesizing visually stunning images given textual instructions. Despite remarkable progress in creating hig…
Tuning Timestep-Distilled Diffusion Model Using Pairwise Sample Optimization
Zichen Miao, Zhengyuan Yang, Kevin Lin +4
Recent advancements in timestep-distilled diffusion models have enabled high-quality image generation that rivals non-distilled multi-step models, but with significantly fewer infe…
AutoDirector: Online Auto-scheduling Agents for Multi-sensory Composition
Minheng Ni, Chenfei Wu, Huaying Yuan +6
With the advancement of generative models, the synthesis of different sensory elements such as music, visuals, and speech has achieved significant realism. However, the approach to…
MM-Vet v2: A Challenging Benchmark to Evaluate Large Multimodal Models for Integrated Capabilities
Weihao Yu, Zhengyuan Yang, Lingfeng Ren +7
MM-Vet, with open-ended vision-language questions targeting at evaluating integrated capabilities, has become one of the most popular benchmarks for large multimodal model evaluati…
IDOL: Unified Dual-Modal Latent Diffusion for Human-Centric Joint Video-Depth Generation
Yuanhao Zhai, Kevin Lin, Linjie Li +7
Significant advances have been made in human-centric video generation, yet the joint video-depth generation problem remains underexplored. Most existing monocular depth estimation…
StrokeNUWA: Tokenizing Strokes for Vector Graphic Synthesis
Zecheng Tang, Chenfei Wu, Zekai Zhang +8
To leverage LLMs for visual synthesis, traditional methods convert raster image information into discrete grid tokens through specialized visual modules, while disrupting the model…
Bring Metric Functions into Diffusion Models
Jie An, Zhengyuan Yang, Jianfeng Wang +4
We introduce a Cascaded Diffusion Model (Cas-DM) that improves a Denoising Diffusion Probabilistic Model (DDPM) by effectively incorporating additional metric functions in training…
Segment and Caption Anything
Xiaoke Huang, Jianfeng Wang, Yansong Tang +5
We propose a method to efficiently equip the Segment Anything Model (SAM) with the ability to generate regional captions. SAM presents strong generalizability to segment anything w…
MM-Narrator: Narrating Long-form Videos with Multimodal In-Context Learning
Chaoyi Zhang, Kevin Lin, Zhengyuan Yang +5
We present MM-Narrator, a novel system leveraging GPT-4 with multimodal in-context learning for the generation of audio descriptions (AD). Unlike previous methods that primarily fo…
GPT-4V in Wonderland: Large Multimodal Models for Zero-Shot Smartphone GUI Navigation
An Yan, Zhengyuan Yang, Wanrong Zhu +9
We present MM-Navigator, a GPT-4V-based agent for the smartphone graphical user interface (GUI) navigation task. MM-Navigator can interact with a smartphone screen as human users,…
OpenLEAF: Open-Domain Interleaved Image-Text Generation and Evaluation
Jie An, Zhengyuan Yang, Linjie Li +5
This work investigates a challenging task named open-domain interleaved image-text generation, which generates interleaved texts and images following an input query. We propose a n…
MM-VID: Advancing Video Understanding with GPT-4V(ision)
Kevin Lin, Faisal Ahmed, Linjie Li +9
We present MM-VID, an integrated system that harnesses the capabilities of GPT-4V, combined with specialized tools in vision, audio, and speech, to facilitate advanced video unders…
The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)
Zhengyuan Yang, Linjie Li, Kevin Lin +4
Large multimodal models (LMMs) extend large language models (LLMs) with multi-sensory skills, such as visual understanding, to achieve stronger generic intelligence. In this paper,…
Idea2Img: Iterative Self-Refinement with GPT-4V(ision) for Automatic Image Design and Generation
Zhengyuan Yang, Jianfeng Wang, Linjie Li +4
We introduce ``Idea to Image,'' a system that enables multimodal iterative self-refinement with GPT-4V(ision) for automatic image design and generation. Humans can quickly identify…
Completing Visual Objects via Bridging Generation and Segmentation
Xiang Li, Yinpeng Chen, Chung-Ching Lin +6
This paper presents a novel approach to object completion, with the primary goal of reconstructing a complete object from its partially visible components. Our method, named MaskCo…