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Exploring Timeline Control for Facial Motion Generation
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Introduces timeline control for facial motion generation: annotates frame-level facial action intervals with TICC time-series clustering, then trains a diffusion model with a base-branch transformer design to generate motions aligned to multi-track timelines, with ChatGPT converting text to timelines.
Problem
Existing facial motion generation is driven by audio or text, which cannot precisely control when specific facial actions occur; frame-level facial action annotations needed for timeline control are also lacking.
Approach
Labor-efficient frame-level annotation of facial action intervals via Toeplitz Inverse Covariance-based Clustering on ARKit/3DMM blendshape time series, plus a diffusion generator with a base network that models global facial couplings and per-region branch networks conditioned on region timelines via cross-attention with re-injected initial timeline tokens.
Key results
- Annotation Macro-F1 of 0.90 (brow), 0.91 (eye), 0.87 (mouth) using TICC, versus 0.73 when using AUs as descriptors.
- Full model achieves TAS 0.84 and SND 0.09 on 100 unseen timelines, with ablations (w/o branch, w/o base, all decouple) degrading accuracy or naturalness.
- User study (21 participants): 92% agree annotations are accurate, 89% agree generated motions match timelines, 86% agree they are natural.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add branch networks for region-specific motion generation (ablation) | Facial motion generation model · Facial motion generation | Timeline Alignment Score (TAS): 0.66 -> 0.84 | Helped |
| Add a base network to encode global motion coupling (ablation) | Facial motion generation model · Facial motion generation | SND: 12.58 -> 4.63 | Helped |
| Retain partial coupling rather than decoupling every facial and head motion (ablation) | Facial motion generation model · Facial motion generation | Timeline Alignment Score (TAS): 0.69 -> 0.84 | Helped |
| Keep the initial timeline tokens at every network layer (ablation) | Facial motion generation model · Facial motion generation | Timeline Alignment Score (TAS): 0.79 -> 0.84 | Helped |
| Use 2 rather than 1 branch-network layer (ablation) | Facial motion generation model · Facial motion generation | Timeline Alignment Score (TAS): 0.76 -> 0.84 | Helped |
| Use 2 rather than 3 branch-network layers (ablation) | Facial motion generation model · Facial motion generation | SND: 5.86 -> 4.63 | Helped |
| Use 2 rather than 4 branch-network layers (ablation) | Facial motion generation model · Facial motion generation | SND: 6.76 -> 4.63 | Helped |
| Use timeline-condition dropout probability 0.5 rather than 0 (ablation) | Facial motion generation model · Facial motion generation | Timeline Alignment Score (TAS): 0.78 -> 0.84 | Helped |
| Use timeline-condition dropout probability 0.5 rather than 0.3 (ablation) | Facial motion generation model · Facial motion generation | Timeline Alignment Score (TAS): 0.83 -> 0.84 | Helped |
| Use timeline-condition dropout probability 0.5 rather than 0.7 (ablation) | Facial motion generation model · Facial motion generation | Timeline Alignment Score (TAS): 0.68 -> 0.84 | Helped |
| Use blendshape descriptors instead of AU descriptors (ablation) | TICC brow annotation · Brow annotation | Macro-F1: 0.73 -> 0.90 | Helped |
Surprising findings
- AutoPlait and CubeMarker failed to produce usable annotations, so TICC was the only workable time-series method.
- Using AU descriptors instead of ARKit blendshapes dropped annotation Macro-F1 from 0.90 to 0.73.
Limitations
- Relies on an in-house blendshape detector and proprietary annotation pipeline; the dataset is not stated as released.
- Comparisons with prior methods (AgentAvatar, InstructAvatar) are qualitative only, since no code is available.
- Fine-grained subcategories (e.g., significant vs. moderate brow raise) are merged into single categories; texture-related motions such as wrinkles are not controlled.
Experimental setup
Datasets: RealTalk
Benchmarks: RealTalk test split
Metrics: Macro-F1 Timeline Alignment Score (TAS) Var FIDfm FID∆fm SND user study agreement
Baselines: AutoPlait CubeMarker AgentAvatar InstructAvatar
Builds on: TICC classifier-free guidance FaceVerse ChatGPT
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