# Breakdance Video classification in the age of Generative AI

arXiv: https://arxiv.org/abs/2510.20287 · HTML: https://mlsanity.com/paper/2510.20287/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 23 Oct 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)

## Builds on (most cited first, 10 of 10)

- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [ImageBind: One Embedding Space To Bind Them All](https://mlsanity.com/paper/2305.05665/) (2023)
- [Tune: A Research Platform for Distributed Model Selection and Training](https://mlsanity.com/paper/1807.05118/) (2018)
- [A Survey on Large Language Models for Recommendation](https://mlsanity.com/paper/2305.19860/) (2023)
- [A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics](https://mlsanity.com/paper/2310.05694/) (2023)
- [A Survey on Content-Aware Video Analysis for Sports](https://mlsanity.com/paper/1703.01170/) (2017)
- [Large Language Models For Text Classification: Case Study And Comprehensive Review](https://mlsanity.com/paper/2501.08457/) (2025)
- [Large Language Models for Social Networks: Applications, Challenges, and Solutions](https://mlsanity.com/paper/2401.02575/) (2024)
- [Sports Intelligence: Assessing the Sports Understanding Capabilities of Language Models through Question Answering from Text to Video](https://mlsanity.com/paper/2406.14877/) (2024)
- [BRACE: The Breakdancing Competition Dataset for Dance Motion Synthesis](https://mlsanity.com/paper/2207.10120/) (2022)

## Abstract (arXiv)

Large Vision Language models have seen huge application in several sports use-cases recently. Most of these works have been targeted towards a limited subset of popular sports like soccer, cricket, basketball etc; focusing on generative tasks like visual question answering, highlight generation. This work analyzes the applicability of the modern video foundation models (both encoder and decoder) for a very niche but hugely popular dance sports - breakdance. Our results show that Video Encoder models continue to outperform state-of-the-art Video Language Models for prediction tasks. We provide insights on how to choose the encoder model and provide a thorough analysis into the workings of a finetuned decoder model for breakdance video classification.