# 3D Pose Based Feedback for Physical Exercises

arXiv: https://arxiv.org/abs/2208.03257 · HTML: https://mlsanity.com/paper/2208.03257/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 43 references that are in the map.
- First posted on arXiv 5 Aug 2022.

## Builds on (most cited first, 28 of 28)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks](https://mlsanity.com/paper/1506.03099/) (2015)
- [NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis](https://mlsanity.com/paper/1604.02808/) (2016)
- [VIBE: Video Inference for Human Body Pose and Shape Estimation](https://mlsanity.com/paper/1912.05656/) (2019)
- [On human motion prediction using recurrent neural networks](https://mlsanity.com/paper/1705.02445/) (2017)
- [3D Human Pose Estimation with Spatial and Temporal Transformers](https://mlsanity.com/paper/2103.10455/) (2021)
- [Soft-DTW: a Differentiable Loss Function for Time-Series](https://mlsanity.com/paper/1703.01541/) (2017)
- [Learning Trajectory Dependencies for Human Motion Prediction](https://mlsanity.com/paper/1908.05436/) (2019)
- [Structural-RNN: Deep Learning on Spatio-Temporal Graphs](https://mlsanity.com/paper/1511.05298/) (2015)
- [Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition](https://mlsanity.com/paper/1904.12659/) (2019)
- [HP-GAN: Probabilistic 3D human motion prediction via GAN](https://mlsanity.com/paper/1711.09561/) (2017)
- [History Repeats Itself: Human Motion Prediction via Motion Attention](https://mlsanity.com/paper/2007.11755/) (2020)
- [Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition](https://mlsanity.com/paper/1607.07043/) (2016)
- [Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition](https://mlsanity.com/paper/1904.01189/) (2019)
- [A Spatio-temporal Transformer for 3D Human Motion Prediction](https://mlsanity.com/paper/2004.08692/) (2020)
- [An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data](https://mlsanity.com/paper/1611.06067/) (2016)
- [Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation](https://mlsanity.com/paper/1804.06055/) (2018)
- [Deep representation learning for human motion prediction and classification](https://mlsanity.com/paper/1702.07486/) (2017)
- [View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition](https://mlsanity.com/paper/1804.07453/) (2018)
- [Multi-Person Extreme Motion Prediction](https://mlsanity.com/paper/2105.08825/) (2021)
- [Learning Human Motion Models for Long-term Predictions](https://mlsanity.com/paper/1704.02827/) (2017)
- [Imitation Learning for Human Pose Prediction](https://mlsanity.com/paper/1909.03449/) (2019)
- [PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervision](https://mlsanity.com/paper/2203.15625/) (2022)
- [Pose Trainer: Correcting Exercise Posture using Pose Estimation](https://mlsanity.com/paper/2006.11718/) (2020)
- [Motion Prediction Using Temporal Inception Module](https://mlsanity.com/paper/2010.03006/) (2020)
- [Dyadic Human Motion Prediction](https://mlsanity.com/paper/2112.00396/) (2021)
- [Long Term Motion Prediction Using Keyposes](https://mlsanity.com/paper/2012.04731/) (2020)

## Built on by (most cited first, 11 of 11)

- [PoseFix: Correcting 3D Human Poses with Natural Language](https://mlsanity.com/paper/2309.08480/) (2023)
- [FLAG3D: A 3D Fitness Activity Dataset with Language Instruction](https://mlsanity.com/paper/2212.04638/) (2022)
- [ExpertAF: Expert Actionable Feedback from Video](https://mlsanity.com/paper/2408.00672/) (2024)
- [Video Action Differencing](https://mlsanity.com/paper/2503.07860/) (2025)
- [Mojito: LLM-Aided Motion Instructor with Jitter-Reduced Inertial Tokens](https://mlsanity.com/paper/2502.16175/) (2025)
- [MAAIG: Motion Analysis And Instruction Generation](https://mlsanity.com/paper/2311.00980/) (2023)
- [Rehabilitation Exercise Quality Assessment through Supervised Contrastive Learning with Hard and Soft Negatives](https://mlsanity.com/paper/2403.02772/) (2024)
- [Online Action Representation using Change Detection and Symbolic Programming](https://mlsanity.com/paper/2405.11511/) (2024)
- [Real-Time Feedback and Benchmark Dataset for Isometric Pose Evaluation](https://mlsanity.com/paper/2506.11774/) (2025)
- [FitAQA: A Benchmark of Fitness Action Quality Assessment for Multimodal Large Language Models](https://mlsanity.com/paper/2608.08736/) (2026)
- [Video-based Exercise Classification and Activated Muscle Group Prediction with Hybrid X3D-SlowFast Network](https://mlsanity.com/paper/2406.06703/) (2024)

## Abstract (arXiv)

Unsupervised self-rehabilitation exercises and physical training can cause serious injuries if performed incorrectly. We introduce a learning-based framework that identifies the mistakes made by a user and proposes corrective measures for easier and safer individual training. Our framework does not rely on hard-coded, heuristic rules. Instead, it learns them from data, which facilitates its adaptation to specific user needs. To this end, we use a Graph Convolutional Network (GCN) architecture acting on the user's pose sequence to model the relationship between the body joints trajectories. To evaluate our approach, we introduce a dataset with 3 different physical exercises. Our approach yields 90.9% mistake identification accuracy and successfully corrects 94.2% of the mistakes.