Human motion capture has become a key tool in various industries, including sports, medical, and character animation for the entertainment sector. Motion capture is used in sports for multiple purposes, including injury prevention, injury analysis, video game industry animations, and even generating informational displays for television broadcasters. Traditional motion capture systems provide solid results in most circumstances. Still, their setup, calibration, and post-processing are expensive and time-consuming, making it difficult to use on a large scale. These concerns are compounded for aquatic activities such as swimming, which pose unique issues such as reflections from markings or the installation of underwater cameras.
Recent developments have made it possible to capture motion from RGB photographs and movies using simple and affordable devices. These real-time single-camera systems could open the door to the widespread application of motion capture during sporting events by utilizing existing live video data. It could be used in small structures to improve the training programs of amateur athletes. However, due to the need for more data, they face several obstacles when using computer vision-based motion capture for swimming. Every Human Pose and Shape (HPS) estimation approach, whether 2D (2D joints, body segmentation) or 3D (3D joints, virtual markers), must extract information from the image. However, computer vision algorithms trained on traditional datasets need help handling aquatic data as they differ greatly from the training images.
Recent advances in HPS estimation demonstrated that synthetic data could replace or complement real images. Introducing SwimXYZ to expand the application of image-based motion capture techniques in swimming. SwimXYZ is an artificial dataset featuring swimming-specific movies annotated with 2D and 3D stitches of real pools. The 3.4 million frames of the 11,520 films that make up SwimXYZ vary in camera perspective, subject and water appearance, lighting and action. Along with 240 synthetic swimming movement sequences in SMPL format, SwimXYZ offers a variety of body shapes and swimming movements.
Researchers from CentraleSupélec, IETR UMR, Centrale Nantes and Université Technologique de Compiègne created SwimXYZ in this study, a sizeable collection of artificial swimming movements and movies that will be available online when the paper is accepted. SwimXYZ’s testing demonstrates the potential for motion capture in swimming, and aims to help make it more widely used. Future studies may employ movements in the SMPL format to train poses and premovements or swimming stroke classifiers, in addition to the movies provided by SwimXYZ to train 2D and 3D pose estimation models. SwimXYZ’s lack of variety in themes (gender, body type, and swimsuit appearance) and locations (outdoor environment, pool floor) can be rectified in future work. Other improvements may include other annotations (such as segmentation and depth maps) or the addition of additional swimming movements such as dips and turns.
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Aneesh Tickoo is a consulting intern at MarktechPost. She is currently pursuing her bachelor’s degree in Data Science and artificial intelligence at the Indian Institute of technology (IIT), Bhilai. She spends most of her time working on projects aimed at harnessing the power of machine learning. Her research interest is image processing and she is passionate about creating solutions around it. She loves connecting with people and collaborating on interesting projects.
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