8 papers
Rosetta: Composable Native Multimodal Pretraining
Xiangyue Liu, Zijian Zhang, Miles Yang +3
Achieving true artificial general intelligence requires foundation models capable of integrating new modalities without forgetting prior knowledge. However, accommodating continuou…
Towards Flexible, Natural, Efficient Interaction for Conversational Talking Face Generation
Baiqin Wang, Sen Chen, Jiankuo Zhao +3
Conversational talking face generation has recently attracted increasing attention, aiming to synthesize interactive talking videos where characters speak, listen, and respond dyna…
Symbiotic-MoE: Unlocking the Synergy between Generation and Understanding
Xiangyue Liu, Zijian Zhang, Miles Yang +3
Empowering Large Multimodal Models (LMMs) with image generation often leads to catastrophic forgetting in understanding tasks due to severe gradient conflicts. While existing parad…
TurboTalk: Progressive Distillation for One-Step Audio-Driven Talking Avatar Generation
Xiangyu Liu, Feng Gao, Xiaomei Zhang +4
Existing audio-driven video digital human generation models rely on multi-step denoising, resulting in substantial computational overhead that severely limits their deployment in r…
InfiniteTalk: Audio-driven Video Generation for Sparse-Frame Video Dubbing
Shaoshu Yang, Zhe Kong, Feng Gao +8
Recent breakthroughs in video AIGC have ushered in a transformative era for audio-driven human animation. However, conventional video dubbing techniques remain constrained to mouth…
NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models
Zeming Li, Xiangyue Liu, Xiangyu Zhang +2
Diffusion models have emerged as powerful generative frameworks, creating data samples by progressively denoising an initial random state. Traditionally, this initial state is samp…