mlsanityData to 14 Oct 2026

MAAIG: Motion Analysis And Instruction Generation

arXiv 2311.00980 ↗First posted 2 Nov 2023cs.CVcited by 1

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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

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2017Neural Discrete Representation Learningcited by 4,712
  3. 2022TEMOS: Generating diverse human motions from textual descriptionscited by 492
  4. 2022TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Textscited by 359
  5. 2016The KIT Motion-Language Datasetcited by 338
  6. 2020HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimationcited by 307
  7. 20223D Pose Based Feedback for Physical Exercisescited by 11

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