Speech-driven 3D facial animation is challenging due to the scarcity of
large-scale visual-audio datasets despite extensive research. Most prior works,
typically focused on learning regression models on a small dataset using the
method of least squares, encounter difficulties generating diverse lip
movements from speech and require substantial effort in refining the generated
outputs. To address these issues, we propose a speech-driven 3D facial
animation with a diffusion model (SAiD), a lightweight Transformer-based U-Net
with a cross-modality alignment bias between audio and visual to enhance lip
synchronization. Moreover, we introduce BlendVOCA, a benchmark dataset of pairs
of speech audio and parameters of a blendshape facial model, to address the
scarcity of public resources. Our experimental results demonstrate that the
proposed approach achieves comparable or superior performance in lip
synchronization to baselines, ensures more diverse lip movements, and
streamlines the animation editing process.
Speech-Driven 3D Facial Animation: Enhancing Lip Synchronization
In the field of multimedia information systems, animations play a crucial role in creating engaging and realistic virtual experiences. One aspect that contributes to the realism of animations is the synchronization of facial movements, particularly lip movements, with speech. This synchronization is challenging due to the scarcity of large-scale visual-audio datasets and the limitations of previous regression models.
The article introduces a novel approach called speech-driven 3D facial animation with a diffusion model (SAiD). SAiD utilizes a lightweight Transformer-based U-Net with a cross-modality alignment bias between audio and visual data. This approach enhances lip synchronization by effectively mapping speech audio to facial movements.
The multidisciplinary nature of this work is evident in the integration of techniques from computer vision, natural language processing, and machine learning. The use of a Transformer-based model allows for capturing complex dependencies between audio and visual features, while the diffusion model enables the generation of diverse lip movements.
To evaluate the proposed approach, the researchers introduce BlendVOCA, a benchmark dataset consisting of pairs of speech audio and parameters of a blendshape facial model. This dataset addresses the scarcity of publicly available resources for training and testing speech-driven facial animation systems.
The experimental results demonstrate that SAiD achieves comparable or even superior performance in lip synchronization when compared to baseline methods. Additionally, SAiD ensures more diverse lip movements, which is essential for creating realistic animations. The proposed approach also streamlines the animation editing process, saving significant effort in refining the generated outputs.
From a holistic perspective, this research contributes to the broader field of multimedia information systems. It addresses the challenges related to speech-driven 3D facial animation, which is crucial for applications such as virtual reality and augmented reality. By enabling more accurate and diverse lip synchronization, SAiD enhances the immersive experience of these technologies.
Overall, this article signifies the significance of advancements in animations, artificial reality, augmented reality, and virtual realities. The proposed approach and dataset pave the way for more sophisticated and realistic multimedia experiences, bridging the gap between audio and visual modalities in virtual environments.