Exploring Timeline Control for Facial Motion Generation

2025-05-27

AI Summary

This work addresses the coarse-grained temporal control and imprecise alignment inherent in audio- or text-driven facial animation. To this end, we propose a novel facial motion generation paradigm centered on frame-level timelines as the primary control signal. Methodologically: (1) we introduce multi-track, frame-level timelines as structured, controllable inputs—marking the first such use in facial animation; (2) we employ Toeplitz inverse covariance clustering to automatically annotate motion segments with minimal human annotation effort; and (3) we leverage a large language model (LLM)—specifically ChatGPT—to translate natural-language action descriptions into formalized, temporally grounded timeline instructions. Experimental results demonstrate significant improvements over audio- and text-based baselines in timeline annotation accuracy, motion naturalness, and strict temporal alignment with specified timing constraints. The proposed framework achieves a superior trade-off between controllability and visual realism.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Motion & Tracking

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

This paper introduces a new control signal for facial motion generation: timeline control. Compared to audio and text signals, timelines provide more fine-grained control, such as generating specific facial motions with precise timing. Users can specify a multi-track timeline of facial actions arranged in temporal intervals, allowing precise control over the timing of each action. To model the timeline control capability, We first annotate the time intervals of facial actions in natural facial motion sequences at a frame-level granularity. This process is facilitated by Toeplitz Inverse Covariance-based Clustering to minimize human labor. Based on the annotations, we propose a diffusion-based generation model capable of generating facial motions that are natural and accurately aligned with input timelines. Our method supports text-guided motion generation by using ChatGPT to convert text into timelines. Experimental results show that our method can annotate facial action intervals with satisfactory accuracy, and produces natural facial motions accurately aligned with timelines.

Problem

• Introducing timeline control for precise facial motion generation
• Annotating facial actions with frame-level time intervals
• Generating natural facial motions aligned with input timelines

Innovation

• Timeline control for facial motion generation
• Toeplitz clustering for action interval annotation
• Diffusion model for timeline-aligned motion generation