activity
20172026
most citedLong-span language modeling for speech recognition

8 citations · 20 across the 47 of their papers we have counts for

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Showing cs.CVShow all

6 papers · 1 filter

cs.CV2026

MOVA: Towards Scalable and Synchronized Video-Audio Generation

OpenMOSS Team, Donghua Yu, Mingshu Chen +38

Audio is indispensable for real-world video, yet generation models have largely overlooked audio components. Current approaches to producing audio-visual content often rely on casc…

cs.CV2026

SenseNova-MARS: Empowering Multimodal Agentic Reasoning and Search via Reinforcement Learning

Yong Xien Chng, Tao Hu, Wenwen Tong +10

While Vision-Language Models (VLMs) can solve complex tasks through agentic reasoning, their capabilities remain largely constrained to text-oriented chain-of-thought or isolated t…

cs.CV2025

IMTalker: Efficient Audio-driven Talking Face Generation with Implicit Motion Transfer

Bo Chen, Tao Liu, Qi Chen +2

Talking face generation aims to synthesize realistic speaking portraits from a single image, yet existing methods often rely on explicit optical flow and local warping, which fail…

cs.CV2025

Bitrate-Controlled Diffusion for Disentangling Motion and Content in Video

Xiao Li, Qi Chen, Xiulian Peng +3

We propose a novel and general framework to disentangle video data into its dynamic motion and static content components. Our proposed method is a self-supervised pipeline with les…

cs.CV2024

VQTalker: Towards Multilingual Talking Avatars through Facial Motion Tokenization

Tao Liu, Ziyang Ma, Qi Chen +4

We present VQTalker, a Vector Quantization-based framework for multilingual talking head generation that addresses the challenges of lip synchronization and natural motion across d…

cs.CV2024

AniTalker: Animate Vivid and Diverse Talking Faces through Identity-Decoupled Facial Motion Encoding

Tao Liu, Feilong Chen, Shuai Fan +4

The paper introduces AniTalker, an innovative framework designed to generate lifelike talking faces from a single portrait. Unlike existing models that primarily focus on verbal cu…