Unified motion-language framework, motion tokenizer, motion-aware language model, and prompt-based question-and-answer tasks for enhanced performance.
Combines language data with large-scale motion models to handle various motion-related tasks.
Converts raw motion data into discrete motion tokens, similar to word tokens.
Learns to understand motion tokens from large language pre-training models by corresponding textual descriptions.
Pre-trains MotionGPT with a mixture of motion-language data and fine-tunes it on prompt-based question-and-answer tasks.
Achieves state-of-the-art performances on multiple motion tasks, including text-driven motion generation, motion captioning, motion prediction, and motion in-betweening.
Text-driven motion generation for animation and gaming applications.
Motion captioning for video analysis and understanding.
Motion prediction for robotics and autonomous systems.
Motion in-betweening for animation and special effects.
Read the MotionGPT paper for a detailed understanding of the model and its applications.
Explore the MotionGPT code repository for implementation details and examples.
Use the MotionGPT model for your specific motion-related task or application.