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

arXiv: https://arxiv.org/abs/2509.02278 · HTML: https://mlsanity.com/paper/2509.02278/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 71 references that are in the map.
- First posted on arXiv 2 Sep 2025.

## Builds on (most cited first, 30 of 38)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Human Motion Diffusion Model](https://mlsanity.com/paper/2209.14916/) (2022)
- [MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model](https://mlsanity.com/paper/2208.15001/) (2022)
- [Action-Conditioned 3D Human Motion Synthesis with Transformer VAE](https://mlsanity.com/paper/2104.05670/) (2021)
- [TEMOS: Generating diverse human motions from textual descriptions](https://mlsanity.com/paper/2204.14109/) (2022)
- [Learning an Animatable Detailed 3D Face Model from In-The-Wild Images](https://mlsanity.com/paper/2012.04012/) (2020)
- [Action2Motion: Conditioned Generation of 3D Human Motions](https://mlsanity.com/paper/2007.15240/) (2020)
- [Capture, Learning, and Synthesis of 3D Speaking Styles](https://mlsanity.com/paper/1905.03079/) (2019)
- [Bailando: 3D Dance Generation by Actor-Critic GPT with Choreographic Memory](https://mlsanity.com/paper/2203.13055/) (2022)
- [FaceFormer: Speech-Driven 3D Facial Animation with Transformers](https://mlsanity.com/paper/2112.05329/) (2021)
- [MeshTalk: 3D Face Animation from Speech using Cross-Modality Disentanglement](https://mlsanity.com/paper/2104.08223/) (2021)
- [CodeTalker: Speech-Driven 3D Facial Animation with Discrete Motion Prior](https://mlsanity.com/paper/2301.02379/) (2023)
- [Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models](https://mlsanity.com/paper/2208.06677/) (2022)
- [EmoTalk: Speech-Driven Emotional Disentanglement for 3D Face Animation](https://mlsanity.com/paper/2303.11089/) (2023)
- [Emotional Speech-Driven Animation with Content-Emotion Disentanglement](https://mlsanity.com/paper/2306.08990/) (2023)
- [X-Portrait: Expressive Portrait Animation with Hierarchical Motion Attention](https://mlsanity.com/paper/2403.15931/) (2024)
- [TM2D: Bimodality Driven 3D Dance Generation via Music-Text Integration](https://mlsanity.com/paper/2304.02419/) (2023)
- [Imitator: Personalized Speech-driven 3D Facial Animation](https://mlsanity.com/paper/2301.00023/) (2022)
- [Talking Head Generation with Probabilistic Audio-to-Visual Diffusion Priors](https://mlsanity.com/paper/2212.04248/) (2022)
- [Visual Speech-Aware Perceptual 3D Facial Expression Reconstruction from Videos](https://mlsanity.com/paper/2207.11094/) (2022)
- [Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions](https://mlsanity.com/paper/1912.10150/) (2019)
- [Beyond Silent Letters: Amplifying LLMs in Emotion Recognition with Vocal Nuances](https://mlsanity.com/paper/2407.21315/) (2024)
- [Beat-It: Beat-Synchronized Multi-Condition 3D Dance Generation](https://mlsanity.com/paper/2407.07554/) (2024)
- [Speech-Driven 3D Face Animation with Composite and Regional Facial Movements](https://mlsanity.com/paper/2308.05428/) (2023)

## Built on by (most cited first, 2 of 2)

- [StableGuard: Towards Unified Copyright Protection and Tamper Localization in Latent Diffusion Models](https://mlsanity.com/paper/2509.17993/) (2025)
- [InterSing: Explicit Interaction Dynamics for 3D Duet Singing Animation and Beyond](https://mlsanity.com/paper/2609.04903/) (2026)

## Abstract (arXiv)

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.