Think2Sing: Singing-Driven 3D Animation

• Think2Sing is a framework that converts singing performances into 3D facial and head animations using FLAME parameters and motion subtitles.
• It integrates lyrics, acoustic descriptors, and LLM-generated subtitles via Sing-CoT and AGRA to ensure temporal consistency and semantic coherence.
• Evaluations demonstrate enhanced lip sync, expressiveness, and beat alignment over traditional speech-driven facial animation methods.

Searching arXiv for Think2Sing and closely related work to ground the article in current papers. Think2Sing is a framework for singing-driven 3D head animation in which a singing performance is converted into a temporally aligned sequence of facial and head motion represented by FLAME parameters. In the formulation used by the paper, each frame-level motion state is $m_i = [\psi_i, \theta_i^{neck}, \theta_i^{jaw}]$, and the full target sequence is $M_{1:L} = \{m_1,\cdots,m_L\}$. The method is explicitly positioned against speech-driven facial animation pipelines that rely mainly on acoustics: it treats singing as a modality with richer emotional nuance, more dynamic prosody, and stronger lyric-conditioned semantics, and therefore inserts an interpretable intermediate representation—motion subtitles—between audio input and 3D motion generation (Huang et al., 2 Sep 2025).

1. Task formulation and domain scope

Think2Sing addresses the problem of generating 3D facial and head animation from singing audio. The target motion is represented by FLAME expression coefficients together with jaw pose and neck pose over time, rather than by raw vertices or by image-space video synthesis. The intended applications named in the paper are virtual avatars, entertainment, education, and AR/VR (Huang et al., 2 Sep 2025).

A central premise of the method is that singing is not adequately modeled as speech with elongated phonemes. The paper attributes the difficulty to three coupled properties. First, singing exhibits richer emotional nuance than ordinary speech. Second, it has more dynamic prosody, especially in pitch and volume variation. Third, its semantics are strongly tied to lyrical intent, which means that identical words may demand different facial realization depending on how they are sung. This produces a multi-timescale synthesis problem involving frame-level visemic events, phrase-level dynamics, and longer emotional arcs.

The framework therefore departs from the standard audio-to-motion pipeline. Rather than predicting full facial motion directly from acoustics, it combines lyrics, acoustic descriptors, an LLM-generated semantic control layer, and a diffusion-based motion generator. A crucial implication is that Think2Sing belongs to the research line of singing-driven animation, not to singing voice synthesis or speech-to-singing conversion.

2. Motion subtitles as the core intermediate representation

The distinctive representation in Think2Sing is the motion subtitle. A motion subtitle is defined as a structured textual description of dynamic motion for a specific head region over a specified time interval. The paper uses an SRT-like format:

$$\texttt{<start\_time> --> <end\_time>: <region> <description>}$$

This representation is not free-form captioning. It is a temporally localized control signal intended to bridge high-level singing semantics and low-level motion trajectories. The paper defines subtitles over four regions: eyebrows, eyes, mouth, and neck pose (Huang et al., 2 Sep 2025).

Region	Subtitle template
Eyebrows, mouth	<motion> <intensity>
Eyes	The eyes <state>.
Neck pose	The head <des_x>, <des_y>, <des_z>

For the eyes, the state vocabulary is explicitly given as $$\{\texttt{widen}, \texttt{squint}, \texttt{close}\}$$. For the neck, the descriptors correspond to motion along vertical, horizontal, and lateral axes. The paper further notes that repeated alternating left-right turns are generalized as “sway to the rhythm” (Huang et al., 2 Sep 2025).

The importance of motion subtitles is threefold. They are interpretable, because a human can inspect the textual priors. They improve temporal consistency, because timestamps constrain when a cue should be active. They also improve semantic coherence, because the subtitles are generated from both lyrical meaning and acoustic delivery. This suggests that Think2Sing treats singing animation as a structured control problem rather than a purely regression-based mapping from waveform to motion.

3. Subtitle generation through Sing-CoT and AGRA

The subtitle-generation pipeline is organized around Singing Chain-of-Thought (Sing-CoT) and Acoustic-Guided Retrieval Augmentation (AGRA). The pipeline begins by extracting time-aligned lyrics and acoustic descriptors from the singing input, then uses an LLM to produce structured motion subtitles. The paper states that the implementation uses Gemini 2.5 Flash as the LLM (Huang et al., 2 Sep 2025).

Sing-CoT decomposes subtitle generation into four stages. The first stage is emotion extraction from lyrics, intended to identify dominant emotional cues from the text. The second is acoustic-guided RAG motion subtitle generation, in which the model retrieves relevant references using lyrics together with acoustic descriptors and then generates region-specific subtitles conditioned on the retrieved material. The third is validation of generated subtitles under three criteria: physical plausibility, formatting correctness, and linguistic diversity. The fourth is feedback reflection, where low-scoring outputs are regenerated with targeted prompt revisions.

AGRA operationalizes retrieval through subtitle-like units extracted with Whisper ASR. The acoustic description is given as

$D_s = \{volume_s, pitch_s, rate_s\},$

where volume, pitch, and singing rate are discretized into low/moderate/high levels. The paper states that these levels are computed per singer using the 25th and 75th percentiles, while global thresholds from the training set are used at inference for unseen singers (Huang et al., 2 Sep 2025).

The subtitle generator is used at inference time as well as during training. During training, annotated subtitles supervise the motion model. During inference, Sing-CoT + AGRA-generated subtitles serve as motion priors. The paper explicitly notes that LLM processing introduces latency, but once subtitles are generated offline, motion inference runs at over 200 FPS on a single RTX 4090 (Huang et al., 2 Sep 2025).

4. Diffusion model, motion-intensity proxy, and semantic modulation

The motion generator in Think2Sing is a diffusion-based model conditioned jointly on acoustics and subtitles. The paper describes a standard forward noising process,

$$q(\mathbf{x}_t|\mathbf{x}_{t-1}) = \mathcal{N}(\mathbf{x}_t;\sqrt{1-\beta_t}\mathbf{x}_{t-1}, \beta_t\mathbf{I}),$$

with $\mathbf{x}_0 = M_{1:L}$ as the target motion sequence. The model is trained, following DDPM, to predict $\mathbf{x}_0$ directly from $\mathbf{x}_t$ (Huang et al., 2 Sep 2025).

Audio is encoded with a pretrained Whisper encoder, whose output is passed to an Audio-driven Motion Initializer producing a shared latent representation $\mathbf{z}$. Subtitle text is encoded by the CLIP text encoder, linearly projected, expanded over the subtitle time interval, and combined with relative positional encoding. The resulting subtitle features are then routed to four region-specific heads: one neck-pose mapper and three intensity mappers for eyebrows, eyes, and mouth.

A major design choice is the reformulation of synthesis as motion intensity prediction rather than direct regression in FLAME or vertex space. The selected landmarks are the union of eyebrow, eye, and mouth landmarks, and landmark motion amplitude is defined relative to a neutral face:

$M_{1:L} = \{m_1,\cdots,m_L\}$0

These amplitudes are normalized over a temporal window into $M_{1:L} = \{m_1,\cdots,m_L\}$1. The paper motivates this proxy by arguing that FLAME’s expression basis couples multiple muscle groups, whereas region-specific intensities preserve interpretability and finer control (Huang et al., 2 Sep 2025).

The conditioning mechanism inside each mapper is the Semantic Modulation layer, consisting of Semantic Alignment and Semantic Injection. In Semantic Alignment, temporal and channel attentions align subtitle features with the latent motion sequence, with temporal attention masked so that it is active only during subtitle-covered intervals. In Semantic Injection, the aligned features are injected by an AdaLN-style modulation. This creates temporally localized, region-specific control.

After intensity prediction, a lightweight Transformer called Intensity2Motion maps estimated intensities to FLAME expression and jaw parameters:

$M_{1:L} = \{m_1,\cdots,m_L\}$2

Training uses three losses at both mapper and Intensity2Motion outputs: reconstruction, velocity, and acceleration, combined in an adaptively weighted total loss according to gradient magnitudes (Huang et al., 2 Sep 2025).

5. SingMoSub dataset and empirical results

Think2Sing is accompanied by SingMoSub, described by the paper as the first multimodal singing dataset for this task containing synchronized singing clips, acoustic descriptors, and motion subtitles. The dataset contains 37.13 hours of singing videos collected from YouTube, BiliBili, and other public repositories. Compared to SingingHead’s 27.10 hours, it is larger in duration and has a much lower Silence Rate: 1.79% versus 16.19% (Huang et al., 2 Sep 2025).

For each frame, FLAME parameters are extracted from monocular video using EMOCA v2. The dataset includes synchronized video, FLAME motion, motion subtitles, and acoustic descriptions. The annotation process is partly automatic. For eyebrows and mouth, it combines framewise Action Unit normalized intensity with vertex-level dynamics under fixed neck pose. Eyes are labeled through eyelid openness relative to neutral FLAME parameters. Neck annotations are based on three-axis rotations with thresholds for insignificant motion. Acoustic descriptions are derived from volume, pitch, and rate. The train:test split is 9:1, and the paper states that songs do not overlap across the split (Huang et al., 2 Sep 2025).

The evaluation compares Think2Sing with FaceFormer, CodeTalker, Imitator, SelfTalk, FaceDiffuser, LG-LDM, and DEEPTalk, retrained on SingMoSub for fairness. To remove identity-shape variation, the experiments use a fixed dummy shape with all-zero shape parameters. The metric suite spans lip sync, geometry, temporal realism, expressiveness, and beat synchrony.

Measure	Think2Sing	Meaning
LVE	8.3036	Lip Vertex Error
FVE	2.1364	Face Vertex Error
Freeze Rate	8.6919%	Below-threshold movement ratio
$M_{1:L} = \{m_1,\cdots,m_L\}$3	4.8187	FID over FLAME parameters
$M_{1:L} = \{m_1,\cdots,m_L\}$4	0.0671	FID over frame differences
SND	4.8858	$M_{1:L} = \{m_1,\cdots,m_L\}$5
FDD	0.1611	Upper-face dynamics deviation
BA	0.2472	Beat Alignment
Trainable parameters	22.14M	Model size

The paper highlights SND 4.8858, compared to the second-best 21.1884, which it describes as a 76.9% reduction. It also reports FDD 0.1611 versus the next-best 0.9744, together with the best BA. The reported interpretation is that some baselines achieve relatively low LVE or FVE by producing nearly static faces, which is why Freeze Rate is introduced as an auxiliary diagnostic (Huang et al., 2 Sep 2025).

A user study with 25 participants over 35 randomly sampled results evaluates lip synchronization, emotional expressiveness, and overall realism, and the paper reports preference for Think2Sing in all three aspects. The ablations are correspondingly central. Replacing motion subtitles with Lyrics Sub degrades performance across LVE, FVE, Freeze Rate, $M_{1:L} = \{m_1,\cdots,m_L\}$6, $M_{1:L} = \{m_1,\cdots,m_L\}$7, SND, FDD, and BA, while GT Sub is close to the full system. This is presented as evidence that AGRA + Sing-CoT subtitles approximate ground-truth annotations much better than raw lyric conditioning. Likewise, replacing the intensity proxy with Direct FLAME or Direct Vert worsens results, especially on overall geometry for direct vertex prediction (Huang et al., 2 Sep 2025).

The subtitle-generation ablation is especially revealing. The reported first-pass validation success rates are 14.18% for the baseline, 18.85% for Lyrics RAG only, 22.95% for AGRA only, 73.13% for Sing-CoT only, and 78.35% for AGRA + Sing-CoT. By the third pass, the success rates become 95.52% for Sing-CoT only and 96.27% for AGRA + Sing-CoT. This indicates that, within the paper’s setup, structured reasoning contributes more than retrieval alone, while retrieval still provides additional gain (Huang et al., 2 Sep 2025).

6. Relation to adjacent singing research and stated limitations

A persistent source of confusion is the name itself. Despite the word “Sing,” Think2Sing is not a singing voice synthesis, singer-cloning, or speech-to-singing model. Its output is a 3D head animation sequence in FLAME space. This distinguishes it from audio-generation systems such as the unsupervised controllable virtual singer framework of “Learn to Sing by Listening” (Xue et al., 2023), the speech-to-singing transfer method “Learning Singing From Speech” (Zhang et al., 2019), the explicit cross-modal speech-to-singing converter AlignSTS (Li et al., 2023), the unified TTS/SVS model UniSyn (Lei et al., 2022), and the technique-controllable multilingual SVS system TechSinger (Guo et al., 18 Feb 2025).

The contrast is methodologically substantive. The singing-synthesis papers listed above manipulate timbre, content, pitch, rhythm, or technique in the acoustic domain. Think2Sing instead manipulates eyebrows, eyes, mouth, and neck pose in a motion domain, using lyrics and acoustics as conditioning rather than as targets. A plausible implication is that Think2Sing extends the study of singing performance from waveform synthesis to cross-modal performance rendering, where semantics and prosody drive visible expression rather than audio alone.

The paper states two explicit limitations. First, because the method uses FLAME, it cannot model phenomena outside the FLAME representation, such as eye-gaze dynamics or hair motion. Second, the LLM-based subtitle generation incurs latency, although the paper partially mitigates this by generating subtitles offline. It also notes that the work does not present extensive failure-case analysis beyond these points, nor does it deeply analyze robustness to poor ASR or unusual singing styles (Huang et al., 2 Sep 2025).

Within the broader literature, Think2Sing can therefore be understood as a specialized response to a gap left by speech-driven facial animation systems: whereas those systems often rely on acoustics alone and produce over-smoothed or emotionally flat motion, Think2Sing inserts a structured semantic layer to preserve region-wise control, temporal consistency, and lyrical coherence. Its significance lies less in vocal synthesis than in re-specifying singing animation as a problem of semantic planning plus motion generation.