PantoMatrix (EMAGE): full-body gesture generation framework, 3D animation framework for generating full-body gestures from audio

General Introduction

PantoMatrix is a state-of-the-art full-body gesture generation framework capable of generating complete human movements from audio and partial gestures, including face, partial body, hand and full-body movements. The framework utilizes the latest multimodal datasets and deep learning techniques to provide high-quality 3D motion capture data suitable for research and educational use.

Function List

• Full Body Gesture Generation: Generate complete human movements from audio and partial gestures.
• Multimodal data sets: Contains high-quality 3D data of face, body, hand and full-body movements.
• speech synchronization: The generated actions are highly synchronized with the audio content.
• High quality 3D animation: Provide community standardized high quality 3D motion capture data.
• Flexible input: Accepts predefined spatio-temporal gesture inputs and generates complete, audio-synchronized results.

Using Help

Installation process

1. Download Code: Visit PantoMatrix's GitHub page to download the latest code base.
2. Installation of dependencies: Install the required dependencies according to the instructions in the README file.
3. Configuration environment: Set up the runtime environment and make sure all dependencies and tools are properly installed.

Usage Process

1. Prepare data: Collect or download the required audio and partial gesture data.
2. operational model: Run the model using the provided script to input audio and gesture data into the model.
3. Generate results: The model will generate complete 3D motion data that users can visualize using 3D animation software.

Detailed Operation Procedure

1. Data preprocessing: Pre-process the audio and gesture data using the tools provided to ensure that the data format conforms to the model requirements.
2. model training: If you need to customize the model, you can use the provided training scripts to train the model and fine-tune it using your own dataset.
3. Visualization of results: Use 3D animation software such as Blender to load the generated 3D motion data for visualization and further editing.

common problems

• How do I get the dataset?: Visit the project page to download the provided multimodal dataset.
• What about slow running models?: Ensure the use of high-performance computing devices or optimize data preprocessing processes.
• What if I generate inaccurate results?: Check the quality of the input data to ensure synchronization and accuracy of the audio and gesture data.

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