19 citations · 26 across the 21 of their papers we have counts for
10 papers · 1 filter
Reading, Writing, Sharing, and Interacting with the World through Video
Shengqiong Wu, Meng Luo, Bobo Li +5
Video foundation models have advanced rapidly in understanding and generating visual sequences, while video world models seek to capture the underlying dynamics of the world they d…
From Evaluation to Enhancement: Benchmarking and Improving Think-with-Video Reasoning for Video Generative Models
Meng Luo, Yicheng Liu, Jiahao Wang +5
Video generation has advanced to produce visually compelling and temporally coherent results. Yet, whether these models can genuinely think with video--executing symbolic rules, re…
V-RAE: Rethinking Video Latent Spaces for Generation
Minghui Guo, Shengqiong Wu, Hao Fei
Latent video generation relies on autoencoders to define a compact space in which generative models operate. Although video autoencoder architectures have evolved substantially, th…
Mental World Modeling
Hao Fei, Yiran Zhao
World models enable a predictive substrate for planning and action, yet existing formulations merely answer a physical question: what/where it is, and how will it evolve. Human beh…
Audio-Visual Intelligence in Large Foundation Models
You Qin, Kai Liu, Shengqiong Wu +12
Audio-Visual Intelligence (AVI) has emerged as a central frontier in artificial intelligence, bridging auditory and visual modalities to enable machines that can perceive, generate…
SCP: Spatial Causal Prediction in Video
Yanguang Zhao, Jie Yang, Shengqiong Wu +9
Spatial reasoning, the ability to understand spatial relations, causality, and dynamic evolution, is central to human intelligence and essential for real-world applications such as…
Synergizing Understanding and Generation with Interleaved Analyzing-Drafting Thinking
Shengqiong Wu, Bobo Li, Xinkai Wang +6
Unified Vision-Language Models (UVLMs) aim to advance multimodal learning by supporting both understanding and generation within a single framework. However, existing approaches la…
Modeling Cross-vision Synergy for Unified Large Vision Model
Shengqiong Wu, Lanhu Wu, Mingyang Bao +5
Recent advances in large vision models (LVMs) have shifted from modality-specific designs toward unified architectures that jointly process images, videos, and 3D data. However, ex…
JavisDiT++: Unified Modeling and Optimization for Joint Audio-Video Generation
Kai Liu, Yanhao Zheng, Kai Wang +7
AIGC has rapidly expanded from text-to-image generation toward high-quality multimodal synthesis across video and audio. Within this context, joint audio-video generation (JAVG) ha…
JavisGPT: A Unified Multi-modal LLM for Sounding-Video Comprehension and Generation
Kai Liu, Jungang Li, Yuchong Sun +13
This paper presents JavisGPT, the first unified multimodal large language model (MLLM) for joint audio-video (JAV) comprehension and generation. JavisGPT has a concise encoder-LLM-…