# TaiChi Action Capture and Performance Analysis with Multi-view RGB Cameras

arXiv: https://arxiv.org/abs/2306.14490 · HTML: https://mlsanity.com/paper/2306.14490/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 36 references that are in the map.
- First posted on arXiv 26 Jun 2023.

## Builds on (most cited first, 20 of 20)

- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding](https://mlsanity.com/paper/1905.04757/) (2019)
- [HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video](https://mlsanity.com/paper/2201.04127/) (2022)
- [Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks](https://mlsanity.com/paper/1711.10305/) (2017)
- [Revisiting Skeleton-based Action Recognition](https://mlsanity.com/paper/2104.13586/) (2021)
- [FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding](https://mlsanity.com/paper/2004.06704/) (2020)
- [FineDiving: A Fine-grained Dataset for Procedure-aware Action Quality Assessment](https://mlsanity.com/paper/2204.03646/) (2022)
- [What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment](https://mlsanity.com/paper/1904.04346/) (2019)
- [Action Quality Assessment Across Multiple Actions](https://mlsanity.com/paper/1812.06367/) (2018)
- [DeepMultiCap: Performance Capture of Multiple Characters Using Sparse Multiview Cameras](https://mlsanity.com/paper/2105.00261/) (2021)
- [SportsCap: Monocular 3D Human Motion Capture and Fine-grained Understanding in Challenging Sports Videos](https://mlsanity.com/paper/2104.11452/) (2021)
- [TransMoMo: Invariance-Driven Unsupervised Video Motion Retargeting](https://mlsanity.com/paper/2003.14401/) (2020)
- [Motion Capture from Internet Videos](https://mlsanity.com/paper/2008.07931/) (2020)
- [Yoga-82: A New Dataset for Fine-grained Classification of Human Poses](https://mlsanity.com/paper/2004.10362/) (2020)
- [Domain Knowledge-Informed Self-Supervised Representations for Workout Form Assessment](https://mlsanity.com/paper/2202.14019/) (2022)
- [GolfDB: A Video Database for Golf Swing Sequencing](https://mlsanity.com/paper/1903.06528/) (2019)
- [Unsupervised 3D Pose Estimation for Hierarchical Dance Video Recognition](https://mlsanity.com/paper/2109.09166/) (2021)

## Built on by (most cited first, 1 of 1)

- [Cross-view Multimodal Vision-Based Assessment Framework for Traditional Chinese Medicine Rehabilitation Training](https://mlsanity.com/paper/2606.28104/) (2026)

## Abstract (arXiv)

Recent advances in computer vision and deep learning have influenced the field of sports performance analysis for researchers to track and reconstruct freely moving humans without any marker attachment. However, there are few works for vision-based motion capture and intelligent analysis for professional TaiChi movement. In this paper, we propose a framework for TaiChi performance capture and analysis with multi-view geometry and artificial intelligence technology. The main innovative work is as follows: 1) A multi-camera system suitable for TaiChi motion capture is built and the multi-view TaiChi data is collected and processed; 2) A combination of traditional visual method and implicit neural radiance field is proposed to achieve sparse 3D skeleton fusion and dense 3D surface reconstruction. 3) The normalization modeling of movement sequences is carried out based on motion transfer, so as to realize TaiChi performance analysis for different groups. We have carried out evaluation experiments, and the experimental results have shown the efficiency of our method.