  Image understanding is a foundational task in computer vision, with recent
applications emerging in soccer posture analysis. However, existing publicly
available datasets lack comprehensive information, notably in the form of
posture sequences and 2D pose annotations. Moreover, current analysis models
often rely on interpretable linear models (e.g., PCA and regression), limiting
their capacity to capture non-linear spatiotemporal relationships in complex
and diverse scenarios. To address these gaps, we introduce the 3D Shot Posture
(3DSP) dataset in soccer broadcast videos, which represents the most extensive
sports image dataset with 2D pose annotations to our knowledge. Additionally,
we present the 3DSP-GRAE (Graph Recurrent AutoEncoder) model, a non-linear
approach for embedding pose sequences. Furthermore, we propose AutoSoccerPose,
a pipeline aimed at semi-automating 2D and 3D pose estimation and posture
analysis. While achieving full automation proved challenging, we provide a
foundational baseline, extending its utility beyond the scope of annotated
data. We validate AutoSoccerPose on SoccerNet and 3DSP datasets, and present
posture analysis results based on 3DSP. The dataset, code, and models are
available at: https://github.com/calvinyeungck/3D-Shot-Posture-Dataset.
