Given a script, the challenge in Movie Dubbing (Visual Voice Cloning, V2C) is to generate speech that aligns well with the video in both time and emotion, based on the tone of a reference audio track. Existing state-of-the-art V2C models break the phonemes in the script according to the divisions between video frames, which solves the temporal alignment problem but leads to incomplete phoneme pronunciation and poor identity stability. To address this problem, we propose StyleDubber, which switches dubbing learning from the frame level to phoneme level. It contains three main components: (1) A multimodal style adaptor operating at the phoneme level to learn pronunciation style from the reference audio, and generate intermediate representations informed by the facial emotion presented in the video; (2) An utterance-level style learning module, which guides both the mel-spectrogram decoding and the refining processes from the intermediate embeddings to improve the overall style expression; And (3) a phoneme-guided lip aligner to maintain lip sync. Extensive experiments on two of the primary benchmarks, V2C and Grid, demonstrate the favorable performance of the proposed method as compared to the current state-of-the-art. The code will be made available at https://github.com/GalaxyCong/StyleDubber.

Generating personalized responses, particularly in the context of video, poses a unique challenge for language models. This paper introduces the novel task of Personalized Video Comment Generation (PVCG), aiming to predict user comments tailored to both the input video and the user’s comment history, where the user is unseen during the model training process. Unlike existing video captioning tasks that ignores the personalization in the text generation process, we introduce PerVidCom, a new dataset specifically collected for this novel task with diverse personalized comments from YouTube. Recognizing the limitations of existing captioning metrics for evaluating this task, we propose a new automatic metric based on Large Language Models (LLMs) with few-shot in-context learning, named FICL-Score, specifically measuring quality from the aspects of emotion, language style and content relevance. We verify the proposed metric with human evaluations. We establish baselines using prominent Multimodal LLMs (MLLMs), analyze their performance discrepancies through extensive evaluation, and identifies directions for future improvement on this important task. Our research opens up a new direction of personalizing MLLMs and paves the way for future research.