4D Sports Scene Reconstruction: From Motion Capture System to Free-Viewpoint Video using Gaussian Splatting
(2026) MAMM15 20261Ergonomics and Aerosol Technology
Department of Design Sciences
- Abstract
- Key actions, tactical cooperation, and spatial relationships in sports activities are usually recorded through 2D videos, which limits users’ ability to understand the game in three-dimensional space. Although professional sports events can provide Free-Viewpoint Video using large-scale camera arrays, such systems are difficult to deploy in low-cost scenarios such as community games and personal training. To address this problem, this thesis presents a 4D sports scene reconstruction and replay pipeline based on motion capture data and 3D Gaussian Splatting representations. The system takes sparse multi-view videos captured by a small number of fixed cameras as input, together with camera calibration, human segmentation, and estimated human... (More)
- Key actions, tactical cooperation, and spatial relationships in sports activities are usually recorded through 2D videos, which limits users’ ability to understand the game in three-dimensional space. Although professional sports events can provide Free-Viewpoint Video using large-scale camera arrays, such systems are difficult to deploy in low-cost scenarios such as community games and personal training. To address this problem, this thesis presents a 4D sports scene reconstruction and replay pipeline based on motion capture data and 3D Gaussian Splatting representations. The system takes sparse multi-view videos captured by a small number of fixed cameras as input, together with camera calibration, human segmentation, and estimated human pose parameters. It reconstructs dynamic human Gaussian avatars and the static environment separately, and integrates them in a unified interactive viewer for scene composition and rendering. To improve sparse-view multi-person reconstruction, the method incorporates pose correction, non-uniform Gaussian initialization, two-stage training, and a perceptually oriented loss-weight setting. The avatar reconstruction component is evaluated on the GalaBasketball and Hi4D datasets using objective metrics, subjective assessment, and ablation studies. Environment reconstruction and full scene replay are demonstrated separately using BASKET-Multiview data with more background views. The results validate the effectiveness of sparse-view avatar reconstruction and demonstrate the feasibility of the complete replay pipeline, while a fully low-camera end-to-end system remains a direction for future deployment. The thesis also discusses limitations in detail recovery, training efficiency, and the use of generative teacher models. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/student-papers/record/9231164
- author
- Jiao, Keming LU
- supervisor
-
- Viktor Larsson LU
- Ludvig Dillén LU
- organization
- course
- MAMM15 20261
- year
- 2026
- type
- H2 - Master's Degree (Two Years)
- subject
- language
- English
- id
- 9231164
- date added to LUP
- 2026-06-04 13:17:33
- date last changed
- 2026-06-09 13:13:45
@misc{9231164,
abstract = {{Key actions, tactical cooperation, and spatial relationships in sports activities are usually recorded through 2D videos, which limits users’ ability to understand the game in three-dimensional space. Although professional sports events can provide Free-Viewpoint Video using large-scale camera arrays, such systems are difficult to deploy in low-cost scenarios such as community games and personal training. To address this problem, this thesis presents a 4D sports scene reconstruction and replay pipeline based on motion capture data and 3D Gaussian Splatting representations. The system takes sparse multi-view videos captured by a small number of fixed cameras as input, together with camera calibration, human segmentation, and estimated human pose parameters. It reconstructs dynamic human Gaussian avatars and the static environment separately, and integrates them in a unified interactive viewer for scene composition and rendering. To improve sparse-view multi-person reconstruction, the method incorporates pose correction, non-uniform Gaussian initialization, two-stage training, and a perceptually oriented loss-weight setting. The avatar reconstruction component is evaluated on the GalaBasketball and Hi4D datasets using objective metrics, subjective assessment, and ablation studies. Environment reconstruction and full scene replay are demonstrated separately using BASKET-Multiview data with more background views. The results validate the effectiveness of sparse-view avatar reconstruction and demonstrate the feasibility of the complete replay pipeline, while a fully low-camera end-to-end system remains a direction for future deployment. The thesis also discusses limitations in detail recovery, training efficiency, and the use of generative teacher models.}},
author = {{Jiao, Keming}},
language = {{eng}},
note = {{Student Paper}},
title = {{4D Sports Scene Reconstruction: From Motion Capture System to Free-Viewpoint Video using Gaussian Splatting}},
year = {{2026}},
}