JavisGPT: A Unified Multi-modal LLM for Sounding-Video Comprehension and Generation
Authors: name: Kai Liu, name: Jungang Li, name: Yuchong Sun, name: Shengqiong Wu, name: Jianzhang Gao, name: Daoan Zhang, name: Wei Zhang, name: Sheng Jin, name: Sicheng Yu, name: Geng Zhan, name: Jiayi Ji, name: Fan Zhou, name: Liang Zheng, name: Shuicheng Yan, name: Hao Fei, name: Tat-Seng Chua
Organization: JavisVerse

This paper presents JavisGPT, the first unified multimodal large language model (MLLM) for Joint Audio-Video (JAV) comprehension and generation. JavisGPT adopts a concise encoder-LLM-decoder architecture, featuring a SyncFusion module for spatio-temporal audio-video fusion and synchrony-aware learnable queries to bridge a pretrained JAV-DiT generator. This design enables temporally coherent video-audio understanding and generation from multimodal instructions. We design an effective three-stage training pipeline consisting of multimodal pretraining, audio-video fine-tuning, and large-scale instruction-tuning, to progressively build multimodal comprehension and generation from existing vision-language models. To support this, we further construct JavisInst-Omni, a high-quality instruction dataset with over 200K GPT-4o-curated audio-video-text dialogues that span diverse and multi-level comprehension and generation scenarios. Extensive experiments on JAV comprehension and generation benchmarks show that JavisGPT outperforms existing MLLMs, particularly in complex and temporally synchronized settings.