1 citations · 1 across the 10 of their papers we have counts for
13 papers
GROW: Group-Relative Advantage-Weighted On-Policy Reinforcement Learning of Autoregressive-Diffusion Text-to-Speech model
Guanrou Yang, Tian Tan, Qian Chen +8
Reinforcement learning for flow-matching text-to-speech is complicated by deterministic ODE sampling: trajectory-level policy-gradient methods typically convert the ODE into an SDE…
OmniVAE: An Audio-Video VAE with Cross-Modal Alignment for Joint Generation
Jun Zhan, Chen Yang, Yitian Gong +23
Recent generative models are moving beyond silent video or standalone audio synthesis toward the joint generation of synchronized audio and video. Despite this progress, jointly ge…
MMAE: A Massive Multitask Audio Editing Benchmark
Ziyang Ma, Ruiqi Yan, Ruiyang Xu +35
We introduce MMAE, a Massive Multitask Audio Editing benchmark, serving as the first comprehensive evaluation testbed designed for general-purpose instruction-based audio editing.…
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…
ViSAudio: End-to-End Video-Driven Binaural Spatial Audio Generation
Mengchen Zhang, Qi Chen, Tong Wu +2
Despite progress in video-to-audio generation, the field focuses predominantly on mono output, lacking spatial immersion. Existing binaural approaches remain constrained by a two-s…
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…
Counting Hallucinations in Diffusion Models
Shuai Fu, Jian Zhou, Qi Chen +7
Diffusion probabilistic models (DPMs) have demonstrated remarkable progress in generative tasks, such as image and video synthesis. However, they still often produce hallucinated s…
ISSE: An Instruction-Guided Speech Style Editing Dataset And Benchmark
Yun Chen, Qi Chen, Zheqi Dai +3
Speech style editing refers to modifying the stylistic properties of speech while preserving its linguistic content and speaker identity. However, most existing approaches depend o…
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…
NTIRE 2025 Challenge on Video Quality Enhancement for Video Conferencing: Datasets, Methods and Results
Varun Jain, Zongwei Wu, Quan Zou +5
This paper presents a comprehensive review of the 1st Challenge on Video Quality Enhancement for Video Conferencing held at the NTIRE workshop at CVPR 2025, and highlights the prob…
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…
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…
GSTalker: Real-time Audio-Driven Talking Face Generation via Deformable Gaussian Splatting
Bo Chen, Shoukang Hu, Qi Chen +4
We present GStalker, a 3D audio-driven talking face generation model with Gaussian Splatting for both fast training (40 minutes) and real-time rendering (125 FPS) with a 35 m…