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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2411.05734 |
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| _version_ | 1866909380780228608 |
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| author | Singh, Agamdeep PB, Sujit Vatsa, Mayank |
| author_facet | Singh, Agamdeep PB, Sujit Vatsa, Mayank |
| contents | Access to expert coaching is essential for developing technique in sports, yet economic barriers often place it out of reach for many enthusiasts. To bridge this gap, we introduce Poze, an innovative video processing framework that provides feedback on human motion, emulating the insights of a professional coach. Poze combines pose estimation with sequence comparison and is optimized to function effectively with minimal data. Poze surpasses state-of-the-art vision-language models in video question-answering frameworks, achieving 70% and 196% increase in accuracy over GPT4V and LLaVAv1.6 7b, respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_05734 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Poze: Sports Technique Feedback under Data Constraints Singh, Agamdeep PB, Sujit Vatsa, Mayank Computer Vision and Pattern Recognition Access to expert coaching is essential for developing technique in sports, yet economic barriers often place it out of reach for many enthusiasts. To bridge this gap, we introduce Poze, an innovative video processing framework that provides feedback on human motion, emulating the insights of a professional coach. Poze combines pose estimation with sequence comparison and is optimized to function effectively with minimal data. Poze surpasses state-of-the-art vision-language models in video question-answering frameworks, achieving 70% and 196% increase in accuracy over GPT4V and LLaVAv1.6 7b, respectively. |
| title | Poze: Sports Technique Feedback under Data Constraints |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.05734 |