Aligning Actions and Walking to LLM-Generated Textual Descriptions
<|reference_start|>Aligning Actions and Walking to LLM-Generated Textual Descriptions: Large Language Models (LLMs) have demonstrated remarkable capabilities in various domains, including data augmentation and synthetic data generation. This work explores the use of LLMs to generate rich textual descriptions for motion sequences, encompassing both actions and walking patterns. We leverage the expressive power of LLMs to align motion representations with high-level linguistic cues, addressing two distinct tasks: action recognition and retrieval of walking sequences based on appearance attributes. For action recognition, we employ LLMs to generate textual descriptions of actions in the BABEL-60 dataset, facilitating the alignment of motion sequences with linguistic representations. In the domain of gait analysis, we investigate the impact of appearance attributes on walking patterns by generating textual descriptions of motion sequences from the DenseGait dataset using LLMs. These descriptions capture subtle variations in walking styles influenced by factors such as clothing choices and footwear. Our approach demonstrates the potential of LLMs in augmenting structured motion attributes and aligning multi-modal representations. The findings contribute to the advancement of comprehensive motion understanding and open up new avenues for leveraging LLMs in multi-modal alignment and data augmentation for motion analysis. We make the code publicly available at https://github.com/Radu1999/WalkAndText<|reference_end|>
@article{chivereanu2024aligning,
    title={Aligning Actions and Walking to LLM-Generated Textual Descriptions},
    author={Radu Chivereanu, Adrian Cosma, Andy Catruna, Razvan Rughinis, Emilian
  Radoi},
    journal={arXiv preprint arXiv:2404.12192},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.12192},
    primaryClass={cs.CV}
}