activity
20242026
most citedAudio-Omni: Extending Multi-modal Understanding to Versatile Audio Generation and Editing

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.SD2026★ 1 cited

Audio-Omni: Extending Multi-modal Understanding to Versatile Audio Generation and Editing

Zeyue Tian, Binxin Yang, Zhaoyang Liu +8

Recent progress in multimodal models has spurred rapid advances in audio understanding, generation, and editing. However, these capabilities are typically addressed by specialized…

cs.MM2025

VAInpaint: Zero-Shot Video-Audio inpainting framework with LLMs-driven Module

Kam Man Wu, Zeyue Tian, Liya Ji +1

Video and audio inpainting for mixed audio-visual content has become a crucial task in multimedia editing recently. However, precisely removing an object and its corresponding audi…

cs.CV2025

ScaleCUA: Scaling Open-Source Computer Use Agents with Cross-Platform Data

Zhaoyang Liu, Jingjing Xie, Zichen Ding +27

Vision-Language Models (VLMs) have enabled computer use agents (CUAs) that operate GUIs autonomously, showing great potential, yet progress is limited by the lack of large-scale, o…

cs.MM2025

AudioX: A Unified Framework for Anything-to-Audio Generation

Zeyue Tian, Zhaoyang Liu, Yizhu Jin +6

Audio and music generation based on flexible multimodal control signals is a widely applicable topic, with the following key challenges: 1) a unified multimodal modeling framework,…

cs.CV2024

HiPrompt: Tuning-free Higher-Resolution Generation with Hierarchical MLLM Prompts

Xinyu Liu, Yingqing He, Lanqing Guo +10

The potential for higher-resolution image generation using pretrained diffusion models is immense, yet these models often struggle with issues of object repetition and structural a…