Model See Model Do: Speech-Driven Facial Animation with Style Control (2505.01319v1)

Abstract: Speech-driven 3D facial animation plays a key role in applications such as virtual avatars, gaming, and digital content creation. While existing methods have made significant progress in achieving accurate lip synchronization and generating basic emotional expressions, they often struggle to capture and effectively transfer nuanced performance styles. We propose a novel example-based generation framework that conditions a latent diffusion model on a reference style clip to produce highly expressive and temporally coherent facial animations. To address the challenge of accurately adhering to the style reference, we introduce a novel conditioning mechanism called style basis, which extracts key poses from the reference and additively guides the diffusion generation process to fit the style without compromising lip synchronization quality. This approach enables the model to capture subtle stylistic cues while ensuring that the generated animations align closely with the input speech. Extensive qualitative, quantitative, and perceptual evaluations demonstrate the effectiveness of our method in faithfully reproducing the desired style while achieving superior lip synchronization across various speech scenarios.

• The paper introduces an example-based framework using latent diffusion models and a novel style basis conditioning mechanism to generate high-fidelity speech-driven facial animations that accurately preserve style nuances from reference videos.
• This methodology improves realism and flexibility by effectively separating time-invariant style traits from dynamic speech-driven movements, demonstrating enhanced performance compared to state-of-the-art baselines.
• The work has practical applications in film, gaming, and VR, offering a scalable solution for example-based style transfer in facial animation, and includes plans to release training code and data for future research.

Model See Model Do: Speech-Driven Facial Animation with Style Control

The paper "Model See Model Do: Speech-Driven Facial Animation with Style Control" introduces an advanced methodology for generating 3D facial animations based on audio input while maintaining stylistic coherence from reference examples. The work seeks to address limitations in previous models, particularly concerning the separation of style and motion dynamics in facial animation synthesis.

Overview and Methodology

The researchers propose an innovative framework that leverages latent diffusion models for the generation of pronounced facial animations, controlled by stylistic references. The central premise is that expressive facial motion can be split into two components: (1) time-invariant expressions capturing long-term stylistic traits and (2) dynamic movements corresponding to the phonetic content of speech. The introduced model combines these components through a diffusion-based approach, allowing for nuanced control over the animation style while ensuring accurate lip synchronization.

A key element of the method is the deployment of a style encoder, jointly trained with the motion diffusion model. Unlike prior models that separate style learning, this integrated approach better distinguishes between inherent speech-driven expressions and variable stylistic elements. The style encoder generates latent style features and a style basis reflecting key static poses derived from reference clips. The diffusion module then iteratively refines the animation, guided by both audio input and stylistic cues.

Contributions and Results

The paper makes significant contributions to enhancing the realism and flexibility of speech-driven facial animations:

1. It introduces an example-based framework for speech-driven facial animation that achieves high fidelity in maintaining style nuances from reference videos. Validation through extensive user studies confirmed the model's effectiveness in accurately preserving these stylistic features.
2. A novel conditioning mechanism, called style basis, is proposed within diffusion-based generation. Experimental ablation studies demonstrated its efficacy in improving motion accuracy and expressiveness.
3. A comprehensive release of the model's training code and data curation pipeline is intended to drive further research, particularly using in-the-wild videos for training and evaluation.

Quantitative measures, including vertex mean squared error (MSE) and lip vertex error (LVE), showcase improved performance compared to state-of-the-art baselines. Additionally, the model provides more accurate lip-sync and facial expression dynamics, as validated in user studies.

Implications and Future Directions

The proposed method holds practical applications in film, video games, and virtual reality, where expressive and stylistically coherent animations are crucial. It provides a flexible and scalable solution, allowing animators and developers to leverage example-based style transfer efficiently, freeing traditional tag-based systems from rigid category constraints.

Looking ahead, the integration of multi-modal conditioning and the application of deep perceptual loss frameworks could further refine this approach. There is also potential in enhancing style control by developing systems that blend motion characteristics from multiple reference clips, expanding the scope of style-driven facial synthesis.

The intersection of style control and animated synthesis explored here signals an exciting development in facial animation research. The work lays a foundation for further exploration into example-based generation techniques, promising future advancements in the creation of expressive digital avatars.