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

• URL: http://arxiv.org/abs/2509.02278v1
• Date: Tue, 02 Sep 2025 12:59:27 GMT
• Title: Think2Sing: Orchestrating Structured Motion Subtitles for Singing-Driven 3D Head Animation
• Authors: Zikai Huang, Yihan Zhou, Xuemiao Xu, Cheng Xu, Xiaofen Xing, Jing Qin, Shengfeng He,
• Abstract summary: Singing involves richer emotional nuance, dynamic prosody, and lyric-based semantics.<n>Existing speech-driven approaches often produce oversimplified, emotionally flat, and semantically inconsistent results.<n>Think2Sing generates semantically coherent and temporally consistent 3D head animations conditioned on both lyrics and acoustics.
• Score: 69.50178144839275
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• Audio Driven Real-Time Facial Animation for Social Telepresence [65.66220599734338]
  We present an audio-driven real-time system for animating photorealistic 3D facial avatars with minimal latency.<n>Central to our approach is an encoder model that transforms audio signals into latent facial expression sequences in real time.<n>We capture the rich spectrum of facial expressions necessary for natural communication while achieving real-time performance.
  arXiv  Detail & Related papers  (2025-10-01T17:57:05Z)
• EmoCAST: Emotional Talking Portrait via Emotive Text Description [56.42674612728354]
  EmoCAST is a diffusion-based framework for precise text-driven emotional synthesis.<n>In appearance modeling, emotional prompts are integrated through a text-guided decoupled emotive module.<n>EmoCAST achieves state-of-the-art performance in generating realistic, emotionally expressive, and audio-synchronized talking-head videos.
  arXiv  Detail & Related papers  (2025-08-28T10:02:06Z)
• OmniHuman-1.5: Instilling an Active Mind in Avatars via Cognitive Simulation [29.41106195298283]
  Existing video avatar models can produce fluid human animations, yet they struggle to move beyond mere physical likeness to capture a character's authentic essence.<n>textbfwe propose a framework designed to generate character animations that are not only physically plausible but also semantically coherent and expressive.
  arXiv  Detail & Related papers  (2025-08-26T17:15:26Z)
• MEDTalk: Multimodal Controlled 3D Facial Animation with Dynamic Emotions by Disentangled Embedding [48.54455964043634]
  MEDTalk is a novel framework for fine-grained and dynamic emotional talking head generation.<n>We integrate audio and speech text, predicting frame-wise intensity variations and dynamically adjusting static emotion features to generate realistic emotional expressions.<n>Our generated results can be conveniently integrated into the industrial production pipeline.
  arXiv  Detail & Related papers  (2025-07-08T15:14:27Z)
• Versatile Multimodal Controls for Expressive Talking Human Animation [26.61771541877306]
  VersaAnimator is a versatile framework that synthesizes expressive talking human videos from arbitrary portrait images.<n>We introduce a token2pose translator to smoothly map 3D motion tokens to 2D pose sequences.
  arXiv  Detail & Related papers  (2025-03-10T08:38:25Z)
• ARTalk: Speech-Driven 3D Head Animation via Autoregressive Model [41.35209566957009]
  Speech-driven 3D facial animation aims to generate realistic lip movements and facial expressions for 3D head models from arbitrary audio clips.<n>We introduce a novel autoregressive model that achieves real-time generation of highly synchronized lip movements and realistic head poses and eye blinks.
  arXiv  Detail & Related papers  (2025-02-27T17:49:01Z)
• Dynamic Typography: Bringing Text to Life via Video Diffusion Prior [73.72522617586593]
  We present an automated text animation scheme, termed "Dynamic Typography" It deforms letters to convey semantic meaning and infuses them with vibrant movements based on user prompts. Our technique harnesses vector graphics representations and an end-to-end optimization-based framework.
  arXiv  Detail & Related papers  (2024-04-17T17:59:55Z)
• 3DiFACE: Diffusion-based Speech-driven 3D Facial Animation and Editing [22.30870274645442]
  We present 3DiFACE, a novel method for personalized speech-driven 3D facial animation and editing. Our method outperforms existing state-of-the-art techniques and yields speech-driven animations with greater fidelity and diversity.
  arXiv  Detail & Related papers  (2023-12-01T19:01:05Z)
• Language-Guided Face Animation by Recurrent StyleGAN-based Generator [87.56260982475564]
  We study a novel task, language-guided face animation, that aims to animate a static face image with the help of languages. We propose a recurrent motion generator to extract a series of semantic and motion information from the language and feed it along with visual information to a pre-trained StyleGAN to generate high-quality frames.
  arXiv  Detail & Related papers  (2022-08-11T02:57:30Z)
• Freeform Body Motion Generation from Speech [53.50388964591343]
  Body motion generation from speech is inherently difficult due to the non-deterministic mapping from speech to body motions. We introduce a novel freeform motion generation model (FreeMo) by equipping a two-stream architecture. Experiments demonstrate the superior performance against several baselines.
  arXiv  Detail & Related papers  (2022-03-04T13:03:22Z)
• Write-a-speaker: Text-based Emotional and Rhythmic Talking-head Generation [28.157431757281692]
  We propose a text-based talking-head video generation framework that synthesizes high-fidelity facial expressions and head motions. Our framework consists of a speaker-independent stage and a speaker-specific stage. Our algorithm achieves high-quality photo-realistic talking-head videos including various facial expressions and head motions according to speech rhythms.
  arXiv  Detail & Related papers  (2021-04-16T09:44:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.