洞見 - Neural Networks - # Joint Expression and Audio-Guided NeRF-based Talking Face Generation

A Novel Method for Simultaneous Facial Expression Transfer and Audio-Driven Lip Synchronization in Talking Face Generation

Q: How can the proposed disentanglement approach be extended to other modalities beyond facial expressions and lip motion, such as body gestures and speech?

The disentanglement approach proposed in JEAN can be effectively extended to other modalities, such as body gestures and speech, by leveraging similar principles of representation learning and self-supervised techniques. For instance, to disentangle body gestures from speech, one could employ a multi-modal framework that captures both audio and visual data from various body movements. This could involve using a combination of pose estimation algorithms to extract keypoints from body movements and audio feature extraction methods to align these gestures with corresponding speech patterns. By applying contrastive learning, as done in the audio encoder of JEAN, one can create embeddings that represent body gestures while ensuring they are distinct from the speech-related features. This would allow for the generation of synchronized body movements that correspond to spoken utterances, enhancing applications in virtual reality, animation, and human-computer interaction. Furthermore, the transformer-based architecture could be adapted to learn long-range dependencies in body movements, similar to how it captures facial expressions, thus enabling a more holistic understanding of human communication that integrates facial expressions, body language, and speech.

Q: What are the potential limitations of the NeRF-based architecture in terms of scalability and generalization to diverse identities and scenarios?

While the NeRF-based architecture in JEAN demonstrates impressive capabilities in generating high-fidelity talking faces, it does face potential limitations regarding scalability and generalization. One significant challenge is the requirement for extensive training data to capture the nuances of different identities and expressions. The current approach relies on monocular videos of specific identities, which may not generalize well to unseen individuals or diverse scenarios. This could lead to overfitting, where the model performs well on the training identities but struggles with new subjects or variations in facial features and expressions. Additionally, the computational complexity of NeRFs can hinder scalability. The volumetric rendering process requires significant computational resources, particularly when generating high-resolution outputs or handling dynamic scenes. This could limit the practical application of the model in real-time scenarios, such as live video conferencing or interactive applications, where quick response times are essential. To address these limitations, future work could explore techniques such as few-shot learning, domain adaptation, or the integration of additional data sources to enhance the model's robustness and scalability across diverse identities and scenarios.

Q: How can the learned expression features be further leveraged for other applications, such as emotion recognition or facial animation?

The learned expression features from JEAN can be leveraged for various applications, including emotion recognition and facial animation, by utilizing their rich, disentangled representations. For emotion recognition, these features can serve as robust input to classifiers that predict emotional states based on facial expressions. By training a supervised model on labeled datasets, the expression features can enhance the accuracy of emotion detection systems, making them more effective in applications such as sentiment analysis, customer feedback systems, and interactive gaming. In the realm of facial animation, the expression features can be utilized to create more realistic and expressive avatars in virtual environments. By mapping the learned features to a set of predefined emotional expressions, developers can generate dynamic animations that respond to user inputs or contextual cues, enhancing user engagement in virtual reality and gaming applications. Furthermore, these features can be integrated into existing animation pipelines to improve the quality of character animations, allowing for more nuanced and lifelike interactions. Overall, the versatility of the learned expression features opens up numerous avenues for innovation in both emotion recognition and facial animation, contributing to more immersive and responsive user experiences across various domains.

核心概念

A novel NeRF-based method that simultaneously combines lip-syncing to a target audio with facial expression transfer to generate high-fidelity talking faces.

摘要

The paper introduces JEAN, a novel method for joint expression and audio-guided NeRF-based talking face generation. The key contributions are:

A self-supervised approach to disentangle facial expressions from lip motion. The method leverages the observation that speech-related mouth motion and expression-related face motion differ temporally and spatially. A self-supervised landmark autoencoder is used to disentangle lip motion from the rest of the face, and a contrastive learning strategy aligns the learned audio features to the lip motion features.
A transformer-based architecture that learns expression features, capturing long-range facial expressions and disentangling them from speech-specific lip motion.
A dynamic NeRF, conditioned on the learned representations for both audio and expression, that can synthesize high-fidelity talking face videos, faithfully following the input facial expressions and speech signal for a given identity.

Quantitative and qualitative evaluations demonstrate that JEAN outperforms state-of-the-art methods in terms of lip synchronization, expression transfer, and identity preservation.

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統計資料

"The same sentence spoken with an angry or happy emotion can have a different meaning."
"Speech-related motion has higher temporal frequency and is spatially localized to the mouth region, while expression-related face motion has a lower temporal frequency and may occur over the entire face region."
"For the same utterance spoken with different expressions, speech-related motion remains consistent."

引述

"We introduce a self-supervised method to extract audio features aligned to lip motion features, achieving accurate lip synchronization on unseen audio."
"We propose a transformer-based module to learn expression features, disentangled from speech-specific lip motion."
"Conditioning on the disentangled representations for expression and audio, we propose a novel NeRF-based method for simultaneous expression control and lip synchronization, outperforming the current state-of-the-art."

從以下內容提煉的關鍵洞見

JEAN: Joint Expression and Audio-guided NeRF-based Talking Face Generation

by Sai Tanmay R... 於 arxiv.org 09-19-2024

https://arxiv.org/pdf/2409.12156.pdf

JEAN: Joint Expression and Audio-guided NeRF-based Talking Face Generation

深入探究

How can the proposed disentanglement approach be extended to other modalities beyond facial expressions and lip motion, such as body gestures and speech?

The disentanglement approach proposed in JEAN can be effectively extended to other modalities, such as body gestures and speech, by leveraging similar principles of representation learning and self-supervised techniques. For instance, to disentangle body gestures from speech, one could employ a multi-modal framework that captures both audio and visual data from various body movements. This could involve using a combination of pose estimation algorithms to extract keypoints from body movements and audio feature extraction methods to align these gestures with corresponding speech patterns.
By applying contrastive learning, as done in the audio encoder of JEAN, one can create embeddings that represent body gestures while ensuring they are distinct from the speech-related features. This would allow for the generation of synchronized body movements that correspond to spoken utterances, enhancing applications in virtual reality, animation, and human-computer interaction. Furthermore, the transformer-based architecture could be adapted to learn long-range dependencies in body movements, similar to how it captures facial expressions, thus enabling a more holistic understanding of human communication that integrates facial expressions, body language, and speech.

What are the potential limitations of the NeRF-based architecture in terms of scalability and generalization to diverse identities and scenarios?

While the NeRF-based architecture in JEAN demonstrates impressive capabilities in generating high-fidelity talking faces, it does face potential limitations regarding scalability and generalization. One significant challenge is the requirement for extensive training data to capture the nuances of different identities and expressions. The current approach relies on monocular videos of specific identities, which may not generalize well to unseen individuals or diverse scenarios. This could lead to overfitting, where the model performs well on the training identities but struggles with new subjects or variations in facial features and expressions.
Additionally, the computational complexity of NeRFs can hinder scalability. The volumetric rendering process requires significant computational resources, particularly when generating high-resolution outputs or handling dynamic scenes. This could limit the practical application of the model in real-time scenarios, such as live video conferencing or interactive applications, where quick response times are essential. To address these limitations, future work could explore techniques such as few-shot learning, domain adaptation, or the integration of additional data sources to enhance the model's robustness and scalability across diverse identities and scenarios.

How can the learned expression features be further leveraged for other applications, such as emotion recognition or facial animation?

The learned expression features from JEAN can be leveraged for various applications, including emotion recognition and facial animation, by utilizing their rich, disentangled representations. For emotion recognition, these features can serve as robust input to classifiers that predict emotional states based on facial expressions. By training a supervised model on labeled datasets, the expression features can enhance the accuracy of emotion detection systems, making them more effective in applications such as sentiment analysis, customer feedback systems, and interactive gaming.
In the realm of facial animation, the expression features can be utilized to create more realistic and expressive avatars in virtual environments. By mapping the learned features to a set of predefined emotional expressions, developers can generate dynamic animations that respond to user inputs or contextual cues, enhancing user engagement in virtual reality and gaming applications. Furthermore, these features can be integrated into existing animation pipelines to improve the quality of character animations, allowing for more nuanced and lifelike interactions.
Overall, the versatility of the learned expression features opens up numerous avenues for innovation in both emotion recognition and facial animation, contributing to more immersive and responsive user experiences across various domains.