MotionGPT: Human Motion as a Foreign Language (2306.14795v2)

Abstract: Though the advancement of pre-trained LLMs unfolds, the exploration of building a unified model for language and other multi-modal data, such as motion, remains challenging and untouched so far. Fortunately, human motion displays a semantic coupling akin to human language, often perceived as a form of body language. By fusing language data with large-scale motion models, motion-language pre-training that can enhance the performance of motion-related tasks becomes feasible. Driven by this insight, we propose MotionGPT, a unified, versatile, and user-friendly motion-LLM to handle multiple motion-relevant tasks. Specifically, we employ the discrete vector quantization for human motion and transfer 3D motion into motion tokens, similar to the generation process of word tokens. Building upon this "motion vocabulary", we perform LLMing on both motion and text in a unified manner, treating human motion as a specific language. Moreover, inspired by prompt learning, we pre-train MotionGPT with a mixture of motion-language data and fine-tune it on prompt-based question-and-answer tasks. Extensive experiments demonstrate that MotionGPT achieves state-of-the-art performances on multiple motion tasks including text-driven motion generation, motion captioning, motion prediction, and motion in-between.

MotionGPT: Human Motion as a Foreign Language

The paper "MotionGPT: Human Motion as a Foreign Language" presents an innovative approach to handling human motion data by treating it similarly to natural language. This work explores the confluence of LLMing and motion understanding, introducing MotionGPT, a versatile model capable of performing various motion-relevant tasks using a single pre-trained architecture.

Problem Statement and State-of-the-Art

Despite significant advancements in pre-trained LLMs, the development of a unified model for both language and multimodal data, specifically human motion, remains an underexplored domain. Existing methods like MDM, MLD, TM2T, and MotionCLIP handle human motion and text separately, treating them as distinct modalities. These models typically require paired motion and text data and lack a comprehensive understanding of the relationship between motion and language.

Contributions

The main contributions of this paper are manifold:

1. Unified Motion-LLM: The introduction of MotionGPT, which integrates language generation capabilities with motion-related tasks by treating human motion as a language. This approach leverages the powerful generative and zero-shot abilities of large pre-trained LLMs.
2. Motion-Language Pre-training: A novel pre-training approach that utilizes a vector quantized variational autoencoder (VQ-VAE) to create a "motion vocabulary", allowing raw motion data to be converted into discrete motion tokens.
3. Diverse Task Handling: By using a unified framework, MotionGPT performs various tasks including text-to-motion, motion-to-text, motion prediction, and motion in-between, showcasing its versatility.

Methodology

MotionGPT's architecture involves several key components and stages:

• Motion Tokenizer: Utilizes a VQ-VAE model to encode raw motion data into discrete motion tokens, similar to how text is tokenized in LLMs.
• Motion-Aware LLM: Combines motion tokens and text tokens into a unified vocabulary. This model processes these tokens using Transformer-based architectures akin to those used in models like T5.
• Two-Stage Training Scheme: The training process is divided into two stages to ensure comprehensive learning:
  1. Pre-training: The model is pre-trained on a mixture of language and motion data to learn the semantic coupling between the two modalities.
  2. Instruction Tuning: Fine-tuning on a prompt-based dataset that includes diverse motion-relevant tasks, enhancing the model's adaptability to various instructions.

Experimental Results

MotionGPT's capabilities were extensively evaluated across multiple benchmarks:

• Text-to-Motion: The model achieved competitive performance metrics, demonstrating its ability to generate motion sequences from textual descriptions accurately.

• Motion-to-Text: MotionGPT outperformed existing models like TM2T in generating natural language descriptions of given motion sequences.
• Motion Prediction and In-between: The model showed superior performance in predicting motion sequences from partial inputs and generating intermediate motions between given start and end frames.

Implications and Future Directions

The broad implication of this research lies in its potential to unify multiple motion-related tasks under a single, adaptable model, reducing the need for task-specific models. This unification could significantly benefit fields like gaming, robotics, and human behavior analysis, where understanding and generating human motion is crucial.

Moving forward, several avenues can be explored to enhance this line of research:

• Expanding to Other Motion Domains: Including facial, hand, and animal motion data to create more comprehensive motion-LLMs.
• Human-Object and Human-Environment Interactions: Modeling more complex scenarios involving interactions between humans and objects or the environment.
• Scalability: Addressing scalability by incorporating larger and more diverse datasets to fully leverage the model’s potential, especially for larger variants of the model.

Conclusion

MotionGPT represents a significant step towards integrating LLMs with human motion understanding. The innovative approach of treating human motion as a language paves the way for more cohesive and versatile models capable of addressing a wide range of motion-related tasks. The promising results and potential applications suggest a bright future for research at the intersection of natural language processing and motion analysis.