Introduction to DiffSHEG
DiffSHEG stands for "Diffusion-Based Approach for Real-Time Speech-driven Holistic 3D Expression and Gesture Generation." It's a cutting-edge project presented at the CVPR 2024 conference. This innovative approach focuses on generating real-time 3D expressions and gestures that are driven by speech input. The project is crafted by a team of experts, including Junming Chen, Yunfei Liu, Jianan Wang, Ailing Zeng, Yu Li, and Qifeng Chen, with contributions from both the Hong Kong University of Science and Technology (HKUST) and the International Digital Economy Academy (IDEA).
Key Features of DiffSHEG
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Real-Time Animation: DiffSHEG is capable of producing dynamic 3D animations that respond to speech in real time. This makes it a powerful tool for applications requiring immediate feedback and interaction, such as virtual reality and digital avatars.
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Diffusion-Based Approach: At the heart of DiffSHEG is a diffusion-based technique. This method ensures smooth transitions and realistic renderings of expressions and gestures based on audio input, enhancing the visual quality and coherence of generated animations.
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Versatile Training Datasets: The project employs comprehensive datasets like BEAT and SHOW, allowing the system to learn and perfect a wide range of expressions and gestures. These datasets help the system to become adaptable to various scenarios and communication styles.
Setting Up the Environment
To work with DiffSHEG, the developers have provided a robust guide for setting up the required environment. It supports installations via conda and pip, ensuring flexibility for users with different preferences. The project recommends using either Ubuntu 18.04 or 20.04 for optimal performance.
Model Training and Inference
DiffSHEG comes with detailed instructions for training models on specific datasets. Users can choose between models trained on the BEAT or SHOW datasets, depending on their requirements. The training procedure leverages multiple CPUs and GPUs to enhance performance and is set up to handle large-scale data efficiently. For inference, users can input custom audio files in the .wav format, allowing the generation of tailored animations.
Testing and Visualization
The project includes extensive testing setups for evaluating the performance of the models. After running tests, results are stored in a specific directory and can be visualized using tools like Blender, or through the TalkSHOW platform for SHOW dataset visualizations. This feature enables users to observe the generated gestures and expressions and fine-tune them if necessary.
Acknowledgments and Resources
DiffSHEG builds on several established projects and frameworks like BEAT, TalkSHOW, and MotionDiffuse. These resources provide a strong foundation and support the project's innovative advancements in speech-driven 3D animation generation.
For those interested in learning more or contributing to DiffSHEG, the project maintains a comprehensive webpage, a published paper, and a demonstration video, all accessible online. The project team encourages anyone who uses their code or finds it helpful to cite their work, ensuring credit is given where due.
Overall, DiffSHEG represents a significant leap forward in the field of real-time 3D expression and gesture generation, promising exciting applications in a variety of digital and interactive environments.