Exploring Vision Transformers for 3D Human Motion-Language Models with Motion Patches (2405.04771v1)

Abstract: To build a cross-modal latent space between 3D human motion and language, acquiring large-scale and high-quality human motion data is crucial. However, unlike the abundance of image data, the scarcity of motion data has limited the performance of existing motion-LLMs. To counter this, we introduce "motion patches", a new representation of motion sequences, and propose using Vision Transformers (ViT) as motion encoders via transfer learning, aiming to extract useful knowledge from the image domain and apply it to the motion domain. These motion patches, created by dividing and sorting skeleton joints based on body parts in motion sequences, are robust to varying skeleton structures, and can be regarded as color image patches in ViT. We find that transfer learning with pre-trained weights of ViT obtained through training with 2D image data can boost the performance of motion analysis, presenting a promising direction for addressing the issue of limited motion data. Our extensive experiments show that the proposed motion patches, used jointly with ViT, achieve state-of-the-art performance in the benchmarks of text-to-motion retrieval, and other novel challenging tasks, such as cross-skeleton recognition, zero-shot motion classification, and human interaction recognition, which are currently impeded by the lack of data.

Cross-Modal Analysis of 3D Human Motion and Language Using Vision Transformers

Introduction to Motion-LLMs and Challenges

The fascinating field of motion-LLMs opens up a plethora of possibilities from animating avatars to generating human motions based on language descriptions. This comes with a significant challenge: the scarcity of large-scale, high-quality human motion data. Unlike the abundant image data available, motion data scarcity severely limits the efficacy of existing models.

Innovative Solution: Motion Patches and Vision Transformers

To tackle the limitations posed by data scarcity in motion-LLMs, the introduction of "motion patches" is a game changer. These patches allow for motion sequences to be represented in a structured way that Vision Transformers (ViT) can process. Here's a breakdown of this novel approach:

• Motion Patches: By dividing and sorting skeleton joints into segments like torso and limbs across motion sequences, motion patches mimic the role of image patches used in ViT, offering a robust way to handle varying skeleton structures.
• Use of ViT: Leveraging Vision Transformers for motion encoding is a clever trick. Originally designed for image classification, applying ViT to motion data (after some clever preprocessing to turn motion into 'images') enables the transfer of robust image analysis capabilities to the domain of motion analysis.

Effective Transfer Learning

Pre-trained on vast image datasets, ViT brings a wealth of knowledge that, when applied to motion, enhances feature extraction profoundly. This transfer learning approach not only addresses the data scarcity by bootstrapping the model with rich, pre-learned features but also aligns well with the structured motion patches to improve overall model performance.

Results & Impact

The utilization of motion patches alongside Vision Transformers has shown to:

• Significantly outperform existing models in text-to-motion retrieval tasks.
• Show promise in novel applications such as cross-skeleton recognition, zero-shot motion classification, and human interaction recognition.

These results underscore the effectiveness of the proposed method in not just matching but potentially exceeding the state of the art in several challenging tasks within the motion-language domain.

Future Implications and Speculations

Looking ahead, the successful application of image-trained models to motion data could signal a broader trend of modal transfer learning, where knowledge is efficiently transferred between radically different types of data. This might open up new avenues in other domains where data scarcity is a challenge.

Furthermore, the idea of motion patches could evolve to handle even more complex interactions involving multiple entities and interactions, making this approach highly scalable and adaptable to future, more complicated datasets.

Practical Applications

From a practical standpoint, the ability to generate and retrieve human motions based on language input has significant implications:

• Animation and Gaming: Streamlining the process of animating characters based on script descriptions.
• Virtual Reality and Augmented Reality: Enhancing user interaction through more intuitive, language-driven motion generation.

By advancing how machines understand and generate human motion from natural language, this research not only pushes the boundaries of AI capabilities in understanding complex, cross-modal datasets but also paves the way for innovative applications that blend the physical with the digital through intuitive, human-like interactions.