Generalized Co-Speech Gesture Synthesis via Semantic Injection with Large-Scale Pre-Training Diffusion Models

Conceitos Básicos

The proposed method, SIGGesture, can synthesize high-quality and semantically relevant 3D co-speech gestures by leveraging large-scale pre-training diffusion models and semantic injection with Large Language Models (LLMs).

Resumo

The key highlights and insights of the content are:

1. The authors introduce SIGGesture, a novel diffusion-based approach for synthesizing realistic co-speech gestures that are both high-quality and semantically pertinent.

2. To address the challenges of generating semantic gestures that follow a long-tailed distribution, the authors leverage the powerful generalization capabilities of LLMs to generate appropriate semantic gestures for various speech transcripts. They propose a semantic injection module to infuse semantic information into the synthesized results during the diffusion reverse process.

3. The authors build a robust diffusion-based foundation model for rhythmical gesture synthesis by pre-training it on a collected large-scale dataset with pseudo labels. This dataset, named Gesture400, is the largest dataset for co-speech gesture synthesis, containing approximately 400 hours of motion sequences.

4. Extensive experiments demonstrate that SIGGesture significantly outperforms existing baselines, exhibiting excellent generalization and controllability. The visualization comparisons indicate that the proposed method produces more stable, expressive, and robust results than other approaches.

5. The authors also discuss the importance of high-quality data collection for data-driven approaches and the limitations of current evaluation metrics in capturing the visual perception of generated gestures.

Estatísticas

"The proposed dataset is the largest dataset for co-speech gesture synthesis, containing approximately 400 hours of motion sequences."
"Extensive experiments show that the proposed method outperforms state-of-the-art methods by a large margin."

Citações

"The automated synthesis of high-quality 3D gestures from speech holds significant value for virtual humans and gaming."
"Semantic gestures are sparse and follow a long-tailed distribution across the gesture sequence, making them challenging to learn in an end-to-end manner."
"Leveraging the strong semantic analysis capabilities of LLMs, the proposed method is adept at handling various languages."

Principais Insights Extraídos De

SIGGesture: Generalized Co-Speech Gesture Synthesis via Semantic Injection with Large-Scale Pre-Training Diffusion Models

by Qingrong Che... às arxiv.org 09-24-2024

https://arxiv.org/pdf/2405.13336.pdf

Perguntas Mais Profundas

How can the proposed method be extended to generate full-body animations with enhanced and detailed expressiveness?

The proposed SIGGesture method can be extended to generate full-body animations by integrating additional modalities and enhancing the existing framework to capture a wider range of body movements and expressions. This can be achieved through the following strategies:

Incorporation of Full-Body Motion Capture Data: By expanding the dataset to include comprehensive full-body motion capture data, the model can learn to synthesize not only gestures but also complex body movements. This would involve collecting high-quality datasets that encompass various body postures, movements, and interactions in different contexts.

Multi-Modal Input Integration: To enhance expressiveness, the model can be designed to accept multiple input modalities, such as facial expressions, emotional cues, and contextual information. This would allow the generation of animations that are not only synchronized with speech but also reflect the emotional state and intent of the speaker.

Hierarchical Modeling: Implementing a hierarchical approach where different levels of motion (e.g., global body movements versus local hand gestures) are modeled separately can improve the overall quality of the generated animations. This would allow for more nuanced control over the expressiveness of the animations, enabling the model to generate detailed and contextually appropriate movements.

Enhanced Semantic Injection: By refining the semantic injection module to include a broader range of semantic gestures and emotional expressions, the model can produce animations that are more aligned with the speaker's intent. This could involve using advanced LLMs to generate a richer set of semantic cues that guide the animation process.

Real-Time Feedback Mechanisms: Incorporating real-time feedback mechanisms that allow the model to adjust animations based on user interactions or environmental changes can lead to more dynamic and engaging full-body animations. This could involve using reinforcement learning techniques to optimize the animation generation process based on user preferences.

By implementing these strategies, the SIGGesture framework can evolve into a robust system capable of generating full-body animations that are not only realistic but also rich in expressiveness and detail.

What are the potential limitations of using LLMs for semantic gesture synthesis, and how can they be addressed?

While the integration of Large Language Models (LLMs) in semantic gesture synthesis offers significant advantages, there are several potential limitations that need to be addressed:

Contextual Understanding Limitations: LLMs may struggle with understanding nuanced contexts or specific cultural references that influence gesture meaning. To address this, the model can be fine-tuned on diverse datasets that include various cultural contexts and conversational scenarios, enhancing its ability to generate contextually appropriate gestures.

Dependency on Training Data: The performance of LLMs is heavily reliant on the quality and diversity of the training data. If the training data lacks representation of certain gestures or contexts, the model may produce biased or inaccurate outputs. To mitigate this, a more extensive and varied dataset should be curated, ensuring that it encompasses a wide range of gestures across different languages and cultures.

Semantic Ambiguity: Language can often be ambiguous, leading to multiple interpretations of the same phrase. This ambiguity can result in the generation of gestures that do not align with the intended meaning. Implementing a feedback loop where human evaluators can provide input on generated gestures can help refine the model's outputs and reduce ambiguity.

Computational Complexity: The integration of LLMs can increase the computational complexity of the gesture synthesis process, potentially leading to slower response times. Optimizing the model architecture and employing techniques such as model distillation can help reduce the computational burden while maintaining performance.

Limited Real-Time Adaptability: LLMs may not adapt well to real-time changes in speech or context during live interactions. To enhance adaptability, the model can be designed to incorporate real-time processing capabilities, allowing it to adjust gestures dynamically based on immediate feedback from the audio input.

By addressing these limitations through targeted strategies, the effectiveness of LLMs in semantic gesture synthesis can be significantly enhanced, leading to more accurate and contextually relevant gesture generation.

How can the evaluation of co-speech gesture synthesis be further improved to better capture the nuances of human perception?

The evaluation of co-speech gesture synthesis can be improved by adopting a more comprehensive and nuanced approach that captures the complexities of human perception. Here are several strategies to enhance evaluation methods:

Multi-Dimensional Evaluation Metrics: Instead of relying solely on quantitative metrics, a multi-dimensional evaluation framework that includes qualitative assessments can provide a more holistic view of gesture quality. This could involve using a combination of user studies, expert evaluations, and automated metrics to assess aspects such as naturalness, expressiveness, and semantic relevance.

Contextualized User Studies: Conducting user studies in varied contexts and with diverse participant groups can help capture a broader range of perceptions. Participants should be asked to evaluate gestures in scenarios that reflect real-world interactions, allowing for insights into how context influences the perception of gestures.

Incorporation of Emotional and Cultural Factors: Evaluations should consider the emotional impact of gestures and how cultural differences affect gesture interpretation. This could involve developing specific metrics that assess the emotional resonance of gestures and their appropriateness in different cultural contexts.

Longitudinal Studies: Implementing longitudinal studies that track user perceptions over time can provide insights into how familiarity with a gesture affects its reception. This approach can help identify trends in user preferences and the long-term effectiveness of gesture synthesis.

Real-Time Feedback Mechanisms: Incorporating real-time feedback mechanisms during evaluations can allow participants to express their perceptions as they observe gestures. This can lead to richer data collection and a better understanding of immediate reactions to synthesized gestures.

Cross-Modal Evaluations: Evaluating gestures in conjunction with other modalities, such as facial expressions and vocal intonations, can provide a more comprehensive understanding of how gestures function within the broader context of communication. This could involve assessing how well gestures complement or enhance other communicative elements.

By implementing these strategies, the evaluation of co-speech gesture synthesis can become more robust, capturing the intricate nuances of human perception and leading to more effective gesture generation systems.