We present a demonstration of a multi-modal 3D capturing platform coupled to a motion comparison system. This work is focused on the preservation of Traditional Sports and Games, namely the Gaelic sports from Ireland and Basque sports from France and Spain. Users can learn, compare and compete in the performance of sporting gestures and compare themselves to real athletes. Our online gesture database provides a way to preserve and display a wide range of sporting gestures. The capturing devices utilised are Kinect 2 sensors and wearable inertial sensors, where the number required varies based on the requested scenario. The fusion of these two capture modalities, coupled to our inverse kinematic algorithm, allow us to synthesize a fluid and reliable 3D model of the user gestures over time. Our novel comparison algorithms provide the user with a performance score and a set of comparison curves (i.e. joint angles and angular velocities), providing a precise and valuable feedback for coaches and players.
In this paper we present a robust and lightweight method for the automatic fitting of deformable 3D face models on facial images. Popular fitting techniques such as those based on statistical models of shape and appearance require a training stage based on a set of facial images and their corresponding facial landmarks, which have to be manually labeled. Therefore, new images in which to fit the model cannot differ too much in shape and appearance (including illumination variation, facial hair, wrinkles, etc) from those used for training. By contrast, our approach can fit a generic face model in two steps: (1) the detection of facial features based on local image gradient analysis and (2) the backprojection of a deformable 3D face model through the optimization of its deformation parameters. The proposed approach can retain the advantages of both learning-free and learning-based approaches. Thus, we can estimate the position, orientation, shape and actions of faces, and initialize user-specific face tracking approaches, such as Online Appearance Models (OAM), which have shown to be more robust * Corresponding author.
Sports are a key part of cultural identity, and it is necessary to preserve them as important intangible Cultural Heritage, especially the human motion techniques specific to individual sports. In this paper we present a method for extracting 3D athlete motion from video broadcast sources, providing an important tool for preserving the heritage represented by these movements. Broadcast videos include camera motion, multiple player interaction, occlusions and noise, presenting significant challenges to solve the reconstruction. The approach requires initial definition of some key-frames and setting of 2D key-points in those frames manually. Thereafter an automatic process estimates the poses and positions of the players in the key-frames, and in the frames between key-frames, taking into account collisions with the environment and human kinematic constraints. Initial results are extremely promising and this data could be used to analyze the sport's evolution over time, or even to generate animations for interactive applications.
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