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The Language of Motion

A unified model of verbal and non-verbal language in 3D human actions

#language model
#multimodal
#avatar
#3D human body movements
#natural communication
The Language of Motion

Product Details

This is a multi-modal language model framework developed by a Stanford University research team that aims to unify verbal and non-verbal language in 3D human movements. The model is capable of understanding and generating multimodal data including text, speech, and motion, which is critical for creating virtual characters that can communicate naturally and is widely used in games, movies, and virtual reality. Key advantages of this model include high flexibility, low training data requirements, and the ability to unlock new tasks such as editable gesture generation and predicting emotions from actions.

Main Features

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- Multi-modal language model: capable of processing multiple input modalities such as text, speech, and motion.
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- Pre-training strategy: Through innovative pre-training strategies, the amount of data required for training is reduced and model performance is improved.
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- Co-speech gesture generation: The model can generate corresponding gestures based on speech.
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- Editable gesture generation: Users can edit and adjust the generated gestures.
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- Text-to-action generation: The model can generate corresponding 3D human actions based on text descriptions.
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- Emotion understanding: The model is able to predict and understand emotions from actions.
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- High Performance: Achieve state-of-the-art performance on co-speech gesture generation tasks.

How to Use

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1. Visit the official website or GitHub page of the model to understand the basic information and functions of the model.
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2. Download and install necessary software dependencies, such as Python environment and deep learning framework.
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3. According to the provided documents, prepare or obtain the required training data, including text, voice and motion data.
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4. Use the pre-training strategy provided by the model to train or fine-tune the model.
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5. Use the trained model to generate or edit 3D human actions, such as co-speech gesture generation or text-to-action generation.
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6. If necessary, further edit and adjust the generated actions to meet specific application requirements.
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7. Integrate the generated actions into games, movies, or virtual reality projects to improve content quality and user experience.

Target Users

The target audience is game developers, filmmakers, virtual reality content creators, and any professional who needs to create or understand 3D human movements. The product helps them create more natural and realistic virtual characters by providing a unified verbal and non-verbal language model, improving the user experience.

Examples

Game developers use this model to generate natural movements and gestures for game characters, improving game immersion.

In film production, models are used to generate character movements based on scripts to speed up the animation production process.

In virtual reality applications, the user's actions and emotions are understood through models to provide a more personalized interactive experience.

Quick Access

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Categories

🔧 other
› AI character generation
› 3D modeling

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