  This paper presents a unified framework to (i) locate the ball, (ii) predict
the pose, and (iii) segment the instance mask of players in team sports scenes.
Those problems are of high interest in automated sports analytics, production,
and broadcast. A common practice is to individually solve each problem by
exploiting universal state-of-the-art models, \eg, Panoptic-DeepLab for player
segmentation. In addition to the increased complexity resulting from the
multiplication of single-task models, the use of the off-the-shelf models also
impedes the performance due to the complexity and specificity of the team
sports scenes, such as strong occlusion and motion blur. To circumvent those
limitations, our paper proposes to train a single model that simultaneously
predicts the ball and the player mask and pose by combining the part intensity
fields and the spatial embeddings principles. Part intensity fields provide the
ball and player location, as well as player joints location. Spatial embeddings
are then exploited to associate player instance pixels to their respective
player center, but also to group player joints into skeletons. We demonstrate
the effectiveness of the proposed model on the DeepSport basketball dataset,
achieving comparable performance to the SoA models addressing each individual
task separately.
