5 papers
InstructDubber: Instruction-based Alignment for Zero-shot Movie Dubbing
Zhedong Zhang, Liang Li, Gaoxiang Cong +5
Movie dubbing seeks to synthesize speech from a given script using a specific voice, while ensuring accurate lip synchronization and emotion-prosody alignment with the character's…
FlowDubber: Movie Dubbing with LLM-based Semantic-aware Learning and Flow Matching based Voice Enhancing
Gaoxiang Cong, Liang Li, Jiadong Pan +5
Movie Dubbing aims to convert scripts into speeches that align with the given movie clip in both temporal and emotional aspects while preserving the vocal timbre of a given brief r…
Collaborative Temporal Consistency Learning for Point-supervised Natural Language Video Localization
Zhuo Tao, Liang Li, Qi Chen +5
Natural language video localization (NLVL) is a crucial task in video understanding that aims to localize the target moment in videos specified by a given language description. Rec…
SafeCFG: Controlling Harmful Features with Dynamic Safe Guidance for Safe Generation
Jiadong Pan, Liang Li, Hongcheng Gao +3
Diffusion models (DMs) have demonstrated exceptional performance in text-to-image tasks, leading to their widespread use. With the introduction of classifier-free guidance (CFG), t…
EmoDubber: Towards High Quality and Emotion Controllable Movie Dubbing
Gaoxiang Cong, Jiadong Pan, Liang Li +5
Given a piece of text, a video clip, and a reference audio, the movie dubbing task aims to generate speech that aligns with the video while cloning the desired voice. The existing…