The DiffuseStyleGesture+ entry to the GENEA Challenge 2023 (2308.13879v1)

Abstract: In this paper, we introduce the DiffuseStyleGesture+, our solution for the Generation and Evaluation of Non-verbal Behavior for Embodied Agents (GENEA) Challenge 2023, which aims to foster the development of realistic, automated systems for generating conversational gestures. Participants are provided with a pre-processed dataset and their systems are evaluated through crowdsourced scoring. Our proposed model, DiffuseStyleGesture+, leverages a diffusion model to generate gestures automatically. It incorporates a variety of modalities, including audio, text, speaker ID, and seed gestures. These diverse modalities are mapped to a hidden space and processed by a modified diffusion model to produce the corresponding gesture for a given speech input. Upon evaluation, the DiffuseStyleGesture+ demonstrated performance on par with the top-tier models in the challenge, showing no significant differences with those models in human-likeness, appropriateness for the interlocutor, and achieving competitive performance with the best model on appropriateness for agent speech. This indicates that our model is competitive and effective in generating realistic and appropriate gestures for given speech. The code, pre-trained models, and demos are available at https://github.com/YoungSeng/DiffuseStyleGesture/tree/DiffuseStyleGesturePlus/BEAT-TWH-main.

• The paper introduces a diffusion model that fuses audio, text, speaker ID, and seed gestures to generate natural conversational gestures.
• It employs advanced feature extraction, frame-level alignment, and cross-local attention to effectively integrate diverse modalities.
• Empirical validation using FGD metrics shows the model achieves human-like performance in gesture naturalness and contextual appropriateness.

An Evaluation of DiffuseStyleGesture+ in the Context of Multimodal Gesture Generation

The paper "The DiffuseStyleGesture+ entry to the GENEA Challenge 2023" presents the authors' contribution to the Generation and Evaluation of Non-verbal Behavior for Embodied Agents (GENEA) Challenge 2023. This challenge aims to advance the creation of automated systems capable of generating natural conversational gestures, a critical area in human-computer interaction research. Within this domain, the DiffuseStyleGesture+ leverages a diffusion model, a relatively novel approach promising superior generation capabilities by maintaining diversity while ensuring the quality of the generated data.

Overview of the DiffuseStyleGesture+ Model

The model utilizes a variety of modalities such as audio, text, speaker identification, and seed gestures, projecting these into a hidden space processed by a diffusion model to create gestures corresponding to provided speech inputs. The core of the DiffuseStyleGesture+ model lies in its ability to blend these modalities effectively, ensuring the generated gestures are coherent and contextually appropriate.

Through careful feature extraction methods for each modality — incorporating advanced techniques such as frame-level alignment and various audio feature representations (MFCC, Mel Spectrum, Pitch, Energy, WavLM, and Onsets) — the authors enhance the robustness of the input data fed into the model. The gesture denoising process is particularly noteworthy, employing linear temporal interpolation for audio features and cross-local attention mechanisms, which aligns modalities effectively, ensuring time and context-sensitive gesture generation.

Experimental Validation and Results

The authors entered their model in the 2023 GENEA Challenge, where it was benchmarked against other approaches in terms of human-likeness, appropriateness for agent speech, and appropriateness for the interlocutor. Results indicated that the DiffuseStyleGesture+ is highly competitive, demonstrating indistinguishable performance from the best models in the human-likeness and interlocutor appropriateness categories. It also achieved comparable outcomes in speech appropriateness metrics.

A notable aspect of the paper is the authors' comprehensive testing and ablation analysis, wherein the effectiveness of the denoising module and input structures were empirically validated. Evaluation metrics such as the Fréchet Gesture Distance (FGD) substantiated claims about the model's proficiency in generating human-like gestures.

Implications and Future Directions

DiffuseStyleGesture+ presents several practical and theoretical implications. The approach demonstrates the potential of diffusion models in generating high-quality, semantically appropriate gestures, which are crucial for developing more natural and intuitive human-computer interaction systems. Furthermore, the model's ability to handle diverse multimodal inputs seamlessly promises advancements in real-time, interactive AI systems.

The paper also presents several avenues for future exploration, particularly in incorporating interlocutor information to improve gesture appropriateness and potentially enhance conversational dynamics. Improving pre-processing techniques and exploring broader and more diverse datasets could further enhance model performance.

The paper wisely refrains from overclaiming, providing a balanced perspective on the model's capabilities while acknowledging areas requiring further improvement. As diffusion models continue to evolve and demonstrate versatility across domains, their application in gesture generation remains a promising research frontier with significant implications for advancing AI-driven communication technologies.