SportsCap: Monocular 3D Human Motion Capture and Fine-grained Understanding in Challenging Sports Videos (2104.11452v4)

Abstract: Markerless motion capture and understanding of professional non-daily human movements is an important yet unsolved task, which suffers from complex motion patterns and severe self-occlusion, especially for the monocular setting. In this paper, we propose SportsCap -- the first approach for simultaneously capturing 3D human motions and understanding fine-grained actions from monocular challenging sports video input. Our approach utilizes the semantic and temporally structured sub-motion prior in the embedding space for motion capture and understanding in a data-driven multi-task manner. To enable robust capture under complex motion patterns, we propose an effective motion embedding module to recover both the implicit motion embedding and explicit 3D motion details via a corresponding mapping function as well as a sub-motion classifier. Based on such hybrid motion information, we introduce a multi-stream spatial-temporal Graph Convolutional Network(ST-GCN) to predict the fine-grained semantic action attributes, and adopt a semantic attribute mapping block to assemble various correlated action attributes into a high-level action label for the overall detailed understanding of the whole sequence, so as to enable various applications like action assessment or motion scoring. Comprehensive experiments on both public and our proposed datasets show that with a challenging monocular sports video input, our novel approach not only significantly improves the accuracy of 3D human motion capture, but also recovers accurate fine-grained semantic action attributes.

Analysis of SportsCap for Monocular 3D Human Motion Capture

The paper "SportsCap: Monocular 3D Human Motion Capture and Fine-grained Understanding in Challenging Sports Videos" introduces a sophisticated approach to capture and understand human motions from monocular video inputs, focusing particularly on complex sports scenarios. The work addresses the inherent challenges posed by severe self-occlusions and advanced motion patterns typical in professional sports movements. The proposed methodology, SportsCap, aims at simultaneously recovering detailed 3D human motion data and producing fine-grained motion analysis.

The key innovation introduced by this paper is a dual-component system that captures 3D motion and semantic action attributes from challenging sports videos using monocular inputs. For context, capturing accurate 3D motions using only monocular video is non-trivial due to depth ambiguities and occlusions. In dealing with these challenges, the authors leverage the semantic and temporally structured sub-motion datasets, forming the basis for an embedding space that aids in recovering both implicit motion embeddings and explicit 3D motion details.

Technical Framework and Methodology

SportsCap’s methodology divides the motion analysis into two primary modules: the Motion Embedding Module and the Action Parsing Module. The Motion Embedding Module utilizes a sub-motion classifier, a CNN encoder, and a novel motion embedding function defined by Principal Component Analysis (PCA) to analyze plausible human poses. The advantage of PCA-driven embedding spaces is their ability to provide structured semantic constraints ensuring realistic motion reconstructions.

The Action Parsing Module employs a sophisticated multi-stream Spatial-Temporal Graph Convolutional Network (ST-GCN) which is built on the joint data and pose parameters retrieved from the Motion Embedding Module. Notably, the multi-stream structure, incorporating pose coefficients alongside joint and bone data, affords an exceptional feature representation for action attributes, which are then mapped to higher-level action labels using a Semantic Attributes Mapping Block. This mechanism offers useful insights for applications like action scoring and assessment.

Dataset Contributions

The authors also introduce the Sports Motion and Recognition Tasks (SMART) dataset, a comprehensive collection of sports videos featuring annotated poses and action labels. The SMART dataset provides essential ground truth data to help refine motion capture systems and is used to validate the performance of the SportsCap model. With over 110,000 frames covering diverse sports activities, it sets a benchmark in the domain of action-specific human modeling, paving the way for robust training and evaluation of monocular capture systems.

Experimental Validations

The experimental results demonstrate that SportsCap significantly enhances the accuracy of 3D human motion capture and semantic action attribute estimation compared to existing state-of-the-art methods like OpenPose and VIBE. The paper reports improvements in performance metrics such as the percentage of correct keypoints and action parsing accuracy, highlighting the strengths of the combined multi-task learning framework. Additionally, the authors conduct a thorough ablation paper and comparative analysis, confirming the advantageous effects of the motion embedding spaces aligned with sub-motion structures.

Implications and Future Directions

The implications of this research are manifold. Practically, it provides robust tools for motion analysis in sports applications, including training enhancement, performance evaluation, and immersive virtual experiences. Theoretically, this work contributes a critical understanding of integrating motion embeddings within action recognition systems, suggesting pathways for future exploration in unconstrained environments.

Potential future developments could focus on extending the framework to dynamic multi-person scenarios, exploring multi-camera setups, or incorporating advanced temporal modeling techniques for enhanced temporal resolutions. Additionally, leveraging developments in NLP could yield richer, narrative-centric motion assessments in sports analytics.

In summary, the paper offers a pivotal advancement in the domain of monocular 3D motion capture, bridging the gap between raw motion data and high-level semantic action understanding in challenging sports contexts. Through innovative architectural designs and comprehensive benchmarks, SportsCap sets a foundation for further investigations into refined motion capture technologies.