  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
