# Mitigating Motion Blur for Robust 3D Baseball Player Pose Modeling for Pitch Analysis

arXiv: https://arxiv.org/abs/2309.01010 · HTML: https://mlsanity.com/paper/2309.01010/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 33 references that are in the map.
- First posted on arXiv 2 Sep 2023.

## Builds on (most cited first, 21 of 21)

- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [End-to-end Recovery of Human Shape and Pose](https://mlsanity.com/paper/1712.06584/) (2017)
- [VIBE: Video Inference for Human Body Pose and Shape Estimation](https://mlsanity.com/paper/1912.05656/) (2019)
- [ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation](https://mlsanity.com/paper/2204.12484/) (2022)
- [XCiT: Cross-Covariance Image Transformers](https://mlsanity.com/paper/2106.09681/) (2021)
- [Convolutional Mesh Regression for Single-Image Human Shape Reconstruction](https://mlsanity.com/paper/1905.03244/) (2019)
- [Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Pose](https://mlsanity.com/paper/2008.09047/) (2020)
- [Learning to Estimate Hidden Motions with Global Motion Aggregation](https://mlsanity.com/paper/2104.02409/) (2021)
- [MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation](https://mlsanity.com/paper/2111.12707/) (2021)
- [Epipolar Transformers](https://mlsanity.com/paper/2005.04551/) (2020)
- [Pose Recognition with Cascade Transformers](https://mlsanity.com/paper/2104.06976/) (2021)
- [Rethinking on Multi-Stage Networks for Human Pose Estimation](https://mlsanity.com/paper/1901.00148/) (2019)
- [Poseur: Direct Human Pose Regression with Transformers](https://mlsanity.com/paper/2201.07412/) (2022)
- [TransFusion: Cross-view Fusion with Transformer for 3D Human Pose Estimation](https://mlsanity.com/paper/2110.09554/) (2021)
- [PE-former: Pose Estimation Transformer](https://mlsanity.com/paper/2112.04981/) (2021)
- [Human from Blur: Human Pose Tracking from Blurry Images](https://mlsanity.com/paper/2303.17209/) (2023)
- [Learning To Describe Player Form in The MLB](https://mlsanity.com/paper/2109.05280/) (2021)

## Built on by (most cited first, 8 of 8)

- [Distribution and Depth-Aware Transformers for 3D Human Mesh Recovery](https://mlsanity.com/paper/2403.09063/) (2024)
- [PitcherNet: Powering the Moneyball Evolution in Baseball Video Analytics](https://mlsanity.com/paper/2405.07407/) (2024)
- [Gen4D: Synthesizing Humans and Scenes in the Wild](https://mlsanity.com/paper/2506.05397/) (2025)
- [Domain-Guided Masked Autoencoders for Unique Player Identification](https://mlsanity.com/paper/2403.11328/) (2024)
- [SportMamba: Adaptive Non-Linear Multi-Object Tracking with State Space Models for Team Sports](https://mlsanity.com/paper/2506.03335/) (2025)
- [DreamPose3D: Hallucinative Diffusion with Prompt Learning for 3D Human Pose Estimation](https://mlsanity.com/paper/2511.09502/) (2025)
- [Avatar4D: Synthesizing Domain-Specific 4D Humans for Real-World Pose Estimation](https://mlsanity.com/paper/2512.16199/) (2025)
- [Scalable Injury-Risk Screening in Baseball Pitching From Broadcast Video](https://mlsanity.com/paper/2603.04864/) (2026)

## Abstract (arXiv)

Using videos to analyze pitchers in baseball can play a vital role in strategizing and injury prevention. Computer vision-based pose analysis offers a time-efficient and cost-effective approach. However, the use of accessible broadcast videos, with a 30fps framerate, often results in partial body motion blur during fast actions, limiting the performance of existing pose keypoint estimation models. Previous works have primarily relied on fixed backgrounds, assuming minimal motion differences between frames, or utilized multiview data to address this problem. To this end, we propose a synthetic data augmentation pipeline to enhance the model's capability to deal with the pitcher's blurry actions. In addition, we leverage in-the-wild videos to make our model robust under different real-world conditions and camera positions. By carefully optimizing the augmentation parameters, we observed a notable reduction in the loss by 54.2% and 36.2% on the test dataset for 2D and 3D pose estimation respectively. By applying our approach to existing state-of-the-art pose estimators, we demonstrate an average improvement of 29.2%. The findings highlight the effectiveness of our method in mitigating the challenges posed by motion blur, thereby enhancing the overall quality of pose estimation.