Think2Sing: Orchestrating Structured Motion Subtitles for Singing-Driven 3D Head Animation

2025-09-02

AI Summary

To address the problems of facial simplification, emotional impoverishment, and semantic disconnection in speech-driven singing animation, this paper proposes a semantics-acoustic joint-driven 3D head animation generation method. Our approach introduces two key innovations: (1) the concept of “motion captions”, which leverages singing chain-of-thought reasoning and acoustic-guided retrieval to produce interpretable, temporally aligned, and region-annotated motion priors; and (2) a diffusion-based framework that integrates a pre-trained large language model with multimodal singing data, formulating animation generation as a facial-region motion intensity prediction task. Experiments on our newly constructed multimodal singing dataset demonstrate that our method significantly outperforms existing approaches in visual realism, emotional fidelity, and expressive diversity. Moreover, it enables fine-grained, user-controllable facial expression editing.

Technology Category

• Natural Language Processing: Generation
• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Singing-driven 3D head animation is a challenging yet promising task with applications in virtual avatars, entertainment, and education. Unlike speech, singing involves richer emotional nuance, dynamic prosody, and lyric-based semantics, requiring the synthesis of fine-grained, temporally coherent facial motion. Existing speech-driven approaches often produce oversimplified, emotionally flat, and semantically inconsistent results, which are insufficient for singing animation. To address this, we propose Think2Sing, a diffusion-based framework that leverages pretrained large language models to generate semantically coherent and temporally consistent 3D head animations, conditioned on both lyrics and acoustics. A key innovation is the introduction of motion subtitles, an auxiliary semantic representation derived through a novel Singing Chain-of-Thought reasoning process combined with acoustic-guided retrieval. These subtitles contain precise timestamps and region-specific motion descriptions, serving as interpretable motion priors. We frame the task as a motion intensity prediction problem, enabling finer control over facial regions and improving the modeling of expressive motion. To support this, we create a multimodal singing dataset with synchronized video, acoustic descriptors, and motion subtitles, enabling diverse and expressive motion learning. Extensive experiments show that Think2Sing outperforms state-of-the-art methods in realism, expressiveness, and emotional fidelity, while also offering flexible, user-controllable animation editing.

Problem

• Generating emotionally nuanced 3D head animation from singing
• Overcoming oversimplified results from speech-driven animation methods
• Creating semantically coherent facial motion synchronized with lyrics

Innovation

• Diffusion-based framework with pretrained language models
• Motion subtitles from Singing Chain-of-Thought reasoning
• Motion intensity prediction for facial region control