Synthetic Data Techniques to Improve Pose Estimation for Wheelchair Users

Core Concepts

Existing pose estimation models perform poorly on wheelchair users due to a lack of representation in training data. This research presents a data synthesis pipeline to generate synthetic data of wheelchair users using motion capture data and motion generation outputs simulated in the Unity game engine, in order to improve pose estimation performance for wheelchair users.

Abstract

The researchers present a novel data synthesis pipeline called WheelPose that leverages motion generation models to simulate highly customizable image data of wheelchair users. The pipeline includes steps for user-defined parameters, data screening, and developer feedback. The generated synthetic data can be used to improve the performance of AI models for wheelchair users in the case of pose estimation.
The researchers evaluate the pipeline by conducting a human evaluation to assess the perceived realism, diversity, and AI performance of the synthetic datasets generated. They find that the generated datasets are perceived as realistic by human evaluators, have more diversity than existing image datasets, and have improved person detection and pose estimation performance when fine-tuned on existing pose estimation models.
The researchers open-source the code for the WheelPose pipeline and provide a fully configurable Unity Environment used to generate the datasets. They also provide detailed instructions on how to source and replace any models they are unable to share due to redistribution and licensing policies.

Stats

The WheelPose dataset contains 70,000 images with 296,508 human instances, of which 271,803 instances have annotated keypoint labeling.
The COCO dataset contains 66,808 images with 273,469 human instances, of which 156,165 have annotated keypoints.

Quotes

"Existing pose estimation models perform poorly on wheelchair users due to a lack of representation in training data."
"Our configurable pipeline generates synthetic data of wheelchair users using motion capture data and motion generation outputs simulated in the Unity game engine."
"We found our generated datasets were perceived as realistic by human evaluators, had more diversity than existing image datasets, and had improved person detection and pose estimation performance when fine-tuned on existing pose estimation models."

Key Insights Distilled From

WheelPose: Data Synthesis Techniques to Improve Pose Estimation Performance on Wheelchair Users

by William Huan... at arxiv.org 04-29-2024

https://arxiv.org/pdf/2404.17063.pdf

Deeper Inquiries

How can the WheelPose pipeline be extended to generate synthetic data for other types of disabilities or assistive technologies beyond wheelchairs?

The WheelPose pipeline can be extended to generate synthetic data for other types of disabilities or assistive technologies by incorporating additional motion capture data specific to those disabilities. For example, for individuals with upper limb amputations, the pipeline can include motion data that reflects the movements and limitations of individuals with amputated arms. This data can be used to generate animations and poses that accurately represent the unique challenges faced by individuals with upper limb amputations. Additionally, the pipeline can be customized to include specific models and objects relevant to different disabilities, such as prosthetic limbs or mobility aids, to create a more realistic simulation environment for training AI models.

What are the potential limitations or biases that could arise from relying too heavily on synthetic data generated through a pipeline like WheelPose?

Relying too heavily on synthetic data generated through a pipeline like WheelPose can introduce several limitations and biases. One potential limitation is the lack of diversity in the synthetic data, as the generated images and poses may not fully capture the range of variations and complexities present in real-world scenarios. This lack of diversity can lead to biased AI models that are not robust enough to handle the complexities of different situations. Additionally, synthetic data may not fully reflect the nuances and intricacies of real human movements, leading to inaccuracies in pose estimation and detection. Another limitation is the potential for overfitting, where AI models trained on synthetic data may not generalize well to real-world scenarios, resulting in poor performance in practical applications.

How can the insights from this research on the importance of user involvement and feedback in the data synthesis process be applied to improve the inclusiveness and fairness of AI systems more broadly?

The insights from this research on the importance of user involvement and feedback in the data synthesis process can be applied to improve the inclusiveness and fairness of AI systems more broadly by prioritizing diverse user representation in the data collection and model training process. By actively involving individuals with disabilities in the data synthesis pipeline, AI researchers can ensure that the generated data accurately reflects the experiences and challenges faced by individuals with different abilities. This user-centric approach can help in creating more inclusive and fair AI models that are sensitive to the needs and perspectives of diverse user groups. Additionally, incorporating user feedback throughout the model development process can help in identifying and addressing biases and limitations in the AI systems, leading to more equitable and unbiased outcomes.