Speech-Driven Holistic 3D Expression and Gesture Generation with Diffusion Models

מושגי ליבה

DiffSHEG, a unified diffusion-based approach, enables the joint generation of synchronized expressions and gestures driven by speech, capturing their inherent relationship through a uni-directional information flow from expression to gesture.

תקציר

The paper proposes DiffSHEG, a unified diffusion-based framework for speech-driven holistic 3D expression and gesture generation.
Key highlights:

DiffSHEG utilizes diffusion models with a unified expression-gesture denoising network, where the uni-directional information flow from expression to gesture is enforced to capture their joint distribution.
The authors introduce a Fast Out-Painting-based Partial Autoregressive Sampling (FOPPAS) method to efficiently generate arbitrary-long smooth motion sequences using diffusion models, enabling real-time streaming inference.
Experiments on two public datasets show that DiffSHEG achieves state-of-the-art performance both quantitatively and qualitatively, generating more realistic, synchronized, and diverse expressions and gestures compared to prior methods.
A user study confirms the superiority of DiffSHEG over previous approaches in terms of motion realism, synchronism, and diversity.

סטטיסטיקה

The training and validation samples are 34-frame clips on the BEAT dataset and 88-frame clips on the SHOW dataset.
The test samples have 64 long sequences with a duration of around 1 minute on the BEAT dataset, and different lengths on the SHOW dataset.

ציטוטים

"To capture the joint distribution, DiffSHEG utilizes diffusion models [16] with a unified expression-gesture denoising network."
"We introduce a Fast Out-Painting-based Partial Autoregressive Sampling (FOPPAS) method to synthesize arbitrary long sequences efficiently."

תובנות מפתח מזוקקות מ:

DiffSHEG

by Junming Chen... ב- arxiv.org 04-09-2024

https://arxiv.org/pdf/2401.04747.pdf

שאלות מעמיקות

How can the proposed uni-directional information flow from expression to gesture be extended to capture more complex relationships between different modalities (e.g., speech, facial expressions, body gestures)?

The uni-directional information flow from expression to gesture proposed in DiffSHEG can be extended to capture more complex relationships between different modalities by incorporating additional layers of abstraction and context. One way to achieve this is by introducing hierarchical structures in the model that can capture the dependencies and interactions between speech, facial expressions, and body gestures at different levels of granularity. For example, the model can have separate branches for processing speech features, facial expression features, and body gesture features, each with its own set of Transformer layers for encoding and decoding the information. These branches can then be interconnected through attention mechanisms to allow for information exchange and fusion between the modalities.
Furthermore, the model can leverage multimodal fusion techniques such as late fusion, early fusion, or cross-modal attention mechanisms to integrate information from different modalities effectively. By incorporating cross-modal attention mechanisms, the model can learn to attend to relevant features in one modality based on the information from another modality, enabling it to capture complex relationships and dependencies between speech, facial expressions, and body gestures.
Additionally, the model can benefit from incorporating contextual information and temporal dependencies to capture the dynamic nature of interactions between different modalities. By considering the temporal evolution of speech, facial expressions, and body gestures over time, the model can better understand the context and nuances of communication, leading to more accurate and nuanced generation of expressions and gestures.

How can the potential limitations of the diffusion-based approach be addressed to further improve the quality and diversity of the generated motions?

Diffusion-based approaches, while effective in capturing complex distributions and generating high-quality motions, may have limitations that can impact the quality and diversity of the generated motions. To address these limitations and further improve the quality and diversity of the generated motions, several strategies can be employed:

Model Architecture Enhancement: Enhance the diffusion model architecture by incorporating additional components such as residual connections, skip connections, or attention mechanisms to facilitate better information flow and gradient propagation. This can help mitigate issues like vanishing gradients and improve the model's ability to capture long-range dependencies.

Data Augmentation: Introduce data augmentation techniques to increase the diversity of the training data and expose the model to a wider range of motion patterns. Techniques such as random cropping, rotation, scaling, and jittering can help the model generalize better and generate more diverse motions.

Regularization: Apply regularization techniques such as dropout, weight decay, or batch normalization to prevent overfitting and encourage the model to learn more robust and generalizable motion representations. Regularization can help improve the model's ability to generate diverse and realistic motions.

Fine-tuning and Transfer Learning: Utilize fine-tuning and transfer learning strategies to leverage pre-trained models or domain-specific knowledge to enhance the model's performance. By fine-tuning the model on specific datasets or tasks, it can adapt better to the target domain and generate more contextually relevant motions.

Ensemble Methods: Explore ensemble methods by combining multiple diffusion models or incorporating different architectures to leverage the strengths of each model. Ensemble methods can help improve the diversity and quality of the generated motions by capturing a broader range of motion patterns and characteristics.

Given the real-time performance of DiffSHEG, how can it be integrated into interactive applications, such as virtual agents or digital humans, to enhance user engagement and experience?

The real-time performance of DiffSHEG makes it well-suited for integration into interactive applications, such as virtual agents or digital humans, to enhance user engagement and experience. Here are some ways in which DiffSHEG can be effectively integrated into interactive applications:

Real-time Interaction: Utilize DiffSHEG to enable real-time generation of expressive and synchronized motions for virtual agents or digital humans in response to user input or dialogue. This can enhance the interactive experience by providing dynamic and engaging visual feedback.

Personalization and Customization: Leverage DiffSHEG to generate personalized gestures and expressions based on user preferences, characteristics, or emotional states. This customization can create a more immersive and tailored experience for users interacting with virtual agents or digital humans.

Enhanced Communication: Use DiffSHEG to enhance communication and expressiveness in virtual environments by enabling virtual agents or digital humans to convey emotions, intentions, and messages through realistic and synchronized gestures and expressions. This can improve the overall user experience and engagement.

Adaptive Behavior: Implement adaptive behavior mechanisms using DiffSHEG to allow virtual agents or digital humans to dynamically adjust their gestures and expressions based on contextual cues, user feedback, or environmental changes. This adaptive behavior can make interactions more natural and responsive.

Multi-modal Interaction: Integrate DiffSHEG with other modalities such as speech recognition, natural language processing, or gaze tracking to enable multi-modal interactions with virtual agents or digital humans. This multi-modal integration can enrich the user experience and facilitate more natural and intuitive communication.

By leveraging the real-time capabilities and high-quality motion generation of DiffSHEG, interactive applications can offer more engaging, immersive, and personalized experiences for users interacting with virtual agents or digital humans.