arXiv:2408.05412v1 Announce Type: new Abstract: Audio-driven lip sync has recently drawn significant attention due to its widespread application in the multimedia domain. Individuals exhibit distinct lip shapes when speaking the same utterance, attributed to the unique speaking styles of individuals, posing a notable challenge for audio-driven lip sync. Earlier methods for such task often bypassed the modeling of personalized speaking styles, resulting in sub-optimal lip sync conforming to the general styles. Recent lip sync techniques attempt to guide the lip sync for arbitrary audio by aggregating information from a style reference video, yet they can not preserve the speaking styles well due to their inaccuracy in style aggregation. This work proposes an innovative audio-aware style reference scheme that effectively leverages the relationships between input audio and reference audio from style reference video to address the style-preserving audio-driven lip sync. Specifically, we first develop an advanced Transformer-based model adept at predicting lip motion corresponding to the input audio, augmented by the style information aggregated through cross-attention layers from style reference video. Afterwards, to better render the lip motion into realistic talking face video, we devise a conditional latent diffusion model, integrating lip motion through modulated convolutional layers and fusing reference facial images via spatial cross-attention layers. Extensive experiments validate the efficacy of the proposed approach in achieving precise lip sync, preserving speaking styles, and generating high-fidelity, realistic talking face videos.
The article “Audio-Driven Lip Sync with Style Preservation” explores the challenges and advancements in audio-driven lip sync technology. Traditionally, lip sync methods have struggled to capture the unique speaking styles of individuals, resulting in sub-optimal synchronization. Recent techniques have attempted to address this issue by incorporating information from style reference videos, but they have been inaccurate in style aggregation. This article introduces an innovative audio-aware style reference scheme that effectively leverages the relationships between input audio and reference audio to preserve speaking styles in lip sync. The proposed approach combines an advanced Transformer-based model for predicting lip motion with a conditional latent diffusion model for rendering realistic talking face videos. Extensive experiments demonstrate the efficacy of the approach in achieving precise lip sync, preserving speaking styles, and generating high-fidelity, realistic videos.
Exploring the Underlying Themes and Concepts of Audio-driven Lip Sync
Audio-driven lip sync has become a popular and important topic in the multimedia domain. The ability to synchronize lip movements with audio can greatly enhance the user experience in various applications, such as animation, virtual reality, and video editing. However, achieving accurate and style-preserving lip sync poses a notable challenge due to the unique speaking styles of individuals.
In the past, lip sync methods often overlooked the modeling of personalized speaking styles, resulting in sub-optimal lip sync that conformed to general styles. This lack of personalized lip syncing limited the realism and effectiveness of the final output. Recent advancements in lip sync techniques have attempted to address this issue by using a style reference video to guide the lip sync process for arbitrary audio. However, these methods still struggle to accurately preserve the speaking styles due to inaccuracies in style aggregation.
An Innovative Approach: Audio-Aware Style Reference Scheme
In order to overcome the limitations of previous lip sync techniques and achieve style-preserving audio-driven lip sync, our work proposes an innovative audio-aware style reference scheme. This scheme effectively leverages the relationships between the input audio and the reference audio from a style reference video.
To begin, we develop an advanced Transformer-based model that is adept at predicting lip motion corresponding to the input audio. This model is augmented by the style information that is aggregated through cross-attention layers from the style reference video. By incorporating style information into the lip motion prediction process, we can better preserve the unique speaking styles of individuals.
Furthermore, we devise a conditional latent diffusion model to better render the predicted lip motion into realistic talking face videos. This model integrates the lip motion through modulated convolutional layers and fuses the reference facial images via spatial cross-attention layers. The combination of these techniques ensures that the final output is both high-fidelity and realistic.
Evaluating the Efficacy of Our Approach
Extensive experiments have been conducted to validate the efficacy of our proposed approach. The results demonstrate that our approach is able to achieve precise lip sync, preserve speaking styles, and generate high-fidelity, realistic talking face videos.
By effectively leveraging the relationships between input audio and reference audio, our audio-aware style reference scheme addresses the challenges of style-preservation in audio-driven lip sync. This innovative approach opens up new possibilities for improving the lip syncing process in various multimedia applications.
“Our proposed approach offers a novel solution to the challenges of audio-driven lip sync. By incorporating personalized speaking styles and leveraging style reference videos, we are able to achieve precise and style-preserving lip sync, ultimately leading to high-fidelity and realistic talking face videos.”
The paper titled “Audio-Driven Lip Sync with Style Preservation” introduces a novel approach to address the challenge of preserving individual speaking styles in audio-driven lip sync. This is a significant problem in the field of multimedia, as individuals have unique lip shapes when speaking the same utterance, which is attributed to their personalized speaking styles.
Previous methods in this area have not adequately modeled personalized speaking styles, resulting in sub-optimal lip sync that conforms to general styles. However, recent techniques have attempted to guide lip sync using a style reference video. These methods aggregate information from the reference video to guide the lip motion for arbitrary audio. However, they have been limited in their ability to accurately preserve the individual speaking styles due to inaccuracies in style aggregation.
To overcome this limitation, the authors propose an innovative audio-aware style reference scheme. They leverage the relationships between the input audio and the reference audio from the style reference video to address the challenge of style-preserving audio-driven lip sync. The proposed approach consists of two main components.
The first component is an advanced Transformer-based model that predicts lip motion corresponding to the input audio. This model is augmented by the style information that is aggregated through cross-attention layers from the style reference video. The use of Transformer-based models is promising, as they have shown excellent performance in various natural language processing tasks.
The second component focuses on rendering the lip motion into realistic talking face videos. To achieve this, the authors devise a conditional latent diffusion model. This model integrates the lip motion through modulated convolutional layers and fuses reference facial images via spatial cross-attention layers. This combination allows for the generation of high-fidelity, realistic talking face videos.
The proposed approach is extensively evaluated through experiments, which validate its efficacy in achieving precise lip sync, preserving speaking styles, and generating high-quality talking face videos. These results indicate the potential of the proposed method to significantly advance the field of audio-driven lip sync.
Looking ahead, this research opens up possibilities for further advancements in the field. One potential direction for future work could be exploring the use of alternative deep learning architectures, such as generative adversarial networks (GANs), to improve the quality and realism of the generated talking face videos. Additionally, investigating the application of this approach to different languages and accents could provide valuable insights into the generalizability of the proposed method. Overall, this paper presents a promising contribution to the field of audio-driven lip sync and sets the stage for future research in this area.
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