ข้อมูลเชิงลึก - Computer Vision - # Co-Speech Gesture Video Generation

TANGO: A Novel Framework for Generating Realistic Co-Speech Gesture Videos Using Retrieval and Diffusion-Based Interpolation

Q: How can the principles of TANGO be applied to generate other forms of non-verbal communication, such as facial expressions or body language, synchronized with speech?

TANGO's core principles are built upon a flexible framework that can be adapted to other forms of non-verbal communication beyond co-speech gestures. Here's how: Modular Adaptation: TANGO's three-step process of graph construction, audio-conditioned retrieval, and diffusion-based interpolation can be tailored to different modalities. Graph Construction: Instead of body poses, the nodes could represent facial expressions (using facial landmarks or Action Units) or broader body language cues (like posture features). Edge creation would rely on similarity metrics relevant to the new modality, such as facial expression dynamics or body language transition smoothness. Audio-Conditioned Retrieval: AuMoCLIP, the hierarchical audio-motion embedding space, can be retrained to learn associations between audio features and the new non-verbal cues. This would involve using datasets with paired audio and corresponding facial expressions or body language annotations. Diffusion-Based Interpolation: ACInterp, the diffusion-based interpolation network, can be fine-tuned to generate visually plausible transitions between facial expressions or body language poses. This would require datasets with high-quality videos of these non-verbal cues. Multi-Modal Integration: A particularly exciting avenue is combining these modalities. Imagine a system that generates synchronized facial expressions, body language, and gestures, creating a more holistic and realistic representation of human communication. This could be achieved by: Unified Graph: Constructing a single graph that incorporates nodes and edges representing all three modalities, allowing for retrieval and interpolation across different non-verbal cues. Hierarchical Embedding: Extending AuMoCLIP to learn joint embeddings across all modalities, capturing the complex interplay between speech and various non-verbal cues.

Q: While TANGO demonstrates impressive results in generating realistic gestures, could its reliance on a pre-existing dataset of gestures limit the diversity and spontaneity of the generated motions compared to human-generated gestures?

You're right to point out the potential limitation of dataset bias. TANGO, like many deep learning models, learns patterns from the data it's trained on. This means: Limited Diversity: If the training dataset lacks diversity in gesture styles, body language nuances, or cultural variations, the generated motions might appear repetitive or skewed towards the dominant styles present in the data. Reduced Spontaneity: Human gestures are often unplanned and driven by subtle emotional cues or contextual factors that might be difficult to capture fully in a dataset. This could lead to generated gestures appearing somewhat "canned" or less natural compared to genuine human expression. However, there are ways to mitigate these limitations and push towards more diverse and spontaneous gesture generation: Diverse and Extensive Datasets: Training on larger, more diverse datasets that encompass a wider range of gesture styles, cultural backgrounds, and emotional expressions is crucial. This would provide the model with a richer vocabulary of motions to draw from. Incorporating Stochasticity: Introducing elements of randomness or probabilistic sampling during the gesture generation process can help break away from deterministic patterns and create more varied and spontaneous motions. Contextual Conditioning: Exploring methods to condition gesture generation on additional contextual factors, such as the emotional tone of the speech, the speaker's personality, or the social setting, could lead to more nuanced and contextually appropriate gestures.

Q: If we consider the generated co-speech gestures as a form of visual language, how might this technology impact the way we communicate and interact with machines in the future, particularly in scenarios involving virtual reality or augmented reality?

The ability to generate realistic and synchronized co-speech gestures has profound implications for human-machine interaction, especially in immersive environments like VR and AR: More Natural and Engaging Interactions: In VR/AR, where embodiment and presence are key, realistic avatars with expressive gestures can significantly enhance the sense of realism and create more engaging and immersive experiences. Users would feel like they are interacting with more believable and relatable virtual beings. Intuitive Communication with Machines: As we move towards more embodied interactions with machines, co-speech gestures could become a natural part of the interface. Imagine controlling smart home devices with a wave of your hand or having a virtual assistant respond with understanding nods and gestures. Accessibility and Inclusivity: This technology has the potential to make communication more accessible for individuals with disabilities. For example, it could be used to create sign language avatars or translate speech to gestures in real-time, bridging communication gaps. New Forms of Storytelling and Entertainment: In VR/AR storytelling, co-speech gestures can add depth and emotional resonance to characters, making narratives more compelling and immersive. This could lead to entirely new forms of interactive entertainment and virtual performances. However, it's important to consider the ethical implications: Realistic Deepfakes: The technology could be misused to create highly convincing deepfakes, blurring the lines between reality and fabrication. Establishing safeguards and ethical guidelines for the use of such technology is crucial. Bias and Representation: As with any AI system, it's essential to ensure that the generated gestures are inclusive and avoid perpetuating harmful stereotypes or biases. Careful dataset curation and algorithmic fairness considerations are paramount.

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TANGO is a novel framework that generates realistic and synchronized co-speech gesture videos by combining a hierarchical audio-motion embedding space for accurate gesture retrieval and a diffusion-based interpolation network for seamless transitions between retrieved video segments.

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arxiv.org

Liu, H., Yang, X., Akiyama, T., Huang, Y., Li, Q., Kuriyama, S., & Taketomi, T. (2024). TANGO: Co-Speech Gesture Video Reenactment with Hierarchical Audio Motion Embedding and Diffusion Interpolation. arXiv preprint arXiv:2410.04221.

This paper introduces TANGO, a novel framework designed to generate high-fidelity, audio-synchronized co-speech gesture videos from a short reference video and target speech audio. The research aims to address the limitations of existing gesture video generation methods, particularly in terms of audio-motion misalignment and visual artifacts.

ข้อมูลเชิงลึกที่สำคัญจาก

TANGO: Co-Speech Gesture Video Reenactment with Hierarchical Audio Motion Embedding and Diffusion Interpolation

by Haiyang Liu,... ที่ arxiv.org 10-08-2024

https://arxiv.org/pdf/2410.04221.pdf

TANGO: Co-Speech Gesture Video Reenactment with Hierarchical Audio Motion Embedding and Diffusion Interpolation

สอบถามเพิ่มเติม

How can the principles of TANGO be applied to generate other forms of non-verbal communication, such as facial expressions or body language, synchronized with speech?

TANGO's core principles are built upon a flexible framework that can be adapted to other forms of non-verbal communication beyond co-speech gestures. Here's how:

Modular Adaptation: TANGO's three-step process of graph construction, audio-conditioned retrieval, and diffusion-based interpolation can be tailored to different modalities.

Graph Construction: Instead of body poses, the nodes could represent facial expressions (using facial landmarks or Action Units) or broader body language cues (like posture features). Edge creation would rely on similarity metrics relevant to the new modality, such as facial expression dynamics or body language transition smoothness.
Audio-Conditioned Retrieval: AuMoCLIP, the hierarchical audio-motion embedding space, can be retrained to learn associations between audio features and the new non-verbal cues. This would involve using datasets with paired audio and corresponding facial expressions or body language annotations.
Diffusion-Based Interpolation: ACInterp, the diffusion-based interpolation network, can be fine-tuned to generate visually plausible transitions between facial expressions or body language poses. This would require datasets with high-quality videos of these non-verbal cues.

Multi-Modal Integration:  A particularly exciting avenue is combining these modalities. Imagine a system that generates synchronized facial expressions, body language, and gestures, creating a more holistic and realistic representation of human communication. This could be achieved by:

Unified Graph: Constructing a single graph that incorporates nodes and edges representing all three modalities, allowing for retrieval and interpolation across different non-verbal cues.
Hierarchical Embedding: Extending AuMoCLIP to learn joint embeddings across all modalities, capturing the complex interplay between speech and various non-verbal cues.

While TANGO demonstrates impressive results in generating realistic gestures, could its reliance on a pre-existing dataset of gestures limit the diversity and spontaneity of the generated motions compared to human-generated gestures?

You're right to point out the potential limitation of dataset bias. TANGO, like many deep learning models, learns patterns from the data it's trained on. This means:

Limited Diversity: If the training dataset lacks diversity in gesture styles, body language nuances, or cultural variations, the generated motions might appear repetitive or skewed towards the dominant styles present in the data.
Reduced Spontaneity:  Human gestures are often unplanned and driven by subtle emotional cues or contextual factors that might be difficult to capture fully in a dataset. This could lead to generated gestures appearing somewhat "canned" or less natural compared to genuine human expression.
However, there are ways to mitigate these limitations and push towards more diverse and spontaneous gesture generation:

Diverse and Extensive Datasets: Training on larger, more diverse datasets that encompass a wider range of gesture styles, cultural backgrounds, and emotional expressions is crucial. This would provide the model with a richer vocabulary of motions to draw from.
Incorporating Stochasticity: Introducing elements of randomness or probabilistic sampling during the gesture generation process can help break away from deterministic patterns and create more varied and spontaneous motions.
Contextual Conditioning:  Exploring methods to condition gesture generation on additional contextual factors, such as the emotional tone of the speech, the speaker's personality, or the social setting, could lead to more nuanced and contextually appropriate gestures.

If we consider the generated co-speech gestures as a form of visual language, how might this technology impact the way we communicate and interact with machines in the future, particularly in scenarios involving virtual reality or augmented reality?

The ability to generate realistic and synchronized co-speech gestures has profound implications for human-machine interaction, especially in immersive environments like VR and AR:

More Natural and Engaging Interactions:  In VR/AR, where embodiment and presence are key, realistic avatars with expressive gestures can significantly enhance the sense of realism and create more engaging and immersive experiences. Users would feel like they are interacting with more believable and relatable virtual beings.
Intuitive Communication with Machines:  As we move towards more embodied interactions with machines, co-speech gestures could become a natural part of the interface. Imagine controlling smart home devices with a wave of your hand or having a virtual assistant respond with understanding nods and gestures.
Accessibility and Inclusivity:  This technology has the potential to make communication more accessible for individuals with disabilities. For example, it could be used to create sign language avatars or translate speech to gestures in real-time, bridging communication gaps.
New Forms of Storytelling and Entertainment:  In VR/AR storytelling, co-speech gestures can add depth and emotional resonance to characters, making narratives more compelling and immersive. This could lead to entirely new forms of interactive entertainment and virtual performances.
However, it's important to consider the ethical implications:

Realistic Deepfakes:  The technology could be misused to create highly convincing deepfakes, blurring the lines between reality and fabrication. Establishing safeguards and ethical guidelines for the use of such technology is crucial.
Bias and Representation:  As with any AI system, it's essential to ensure that the generated gestures are inclusive and avoid perpetuating harmful stereotypes or biases. Careful dataset curation and algorithmic fairness considerations are paramount.