MAAIG: Motion Analysis And Instruction Generation
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Abstract (arXiv)
Many people engage in self-directed sports training at home but lack the real-time guidance of professional coaches, making them susceptible to injuries or the development of incorrect habits. In this paper, we propose a novel application framework called MAAIG(Motion Analysis And Instruction Generation). It can generate embedding vectors for each frame based on user-provided sports action videos. These embedding vectors are associated with the 3D skeleton of each frame and are further input into a pretrained T5 model. Ultimately, our model utilizes this information to generate specific sports instructions. It has the capability to identify potential issues and provide real-time guidance in a manner akin to professional coaches, helping users improve their sports skills and avoid injuries.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 15 references that are in the map. First posted on arXiv 2 Nov 2023.
Builds on
7 of 15 references.
References · 7
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
- 2017Neural Discrete Representation Learningcited by 4,712
- 2022TEMOS: Generating diverse human motions from textual descriptionscited by 492
- 2022TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Textscited by 359
- 2016The KIT Motion-Language Datasetcited by 338
- 2020HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimationcited by 307
- 20223D Pose Based Feedback for Physical Exercisescited by 11
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Citing papers per year: 2026: 1.
1 paper.
- 2026A Survey of Large Models in Sportscited by 0
- 2026A Survey of Large Models in Sports14 Aug 2026