We present a novel method for real-time continuous pose recovery of markerless complex articulable objects from a single depth image. Our method consists of the following stages: a randomized decision forest classifier for image segmentation, a robust method for labeled dataset generation, a convolutional network for dense feature extraction, and finally an inverse kinematics stage for stable real-time pose recovery. As one possible application of this pipeline, we show state-of-the-art results for real-time puppeteering of a skinned hand-model.

The present research examined how mode of play in an educational mathematics video game impacts leaming, performance, and motivation. The game was designed for the practice and automation of arithmetic skills to increase fluency and was adapted to allow for individual, competitive, or collaborative game play. Participants (N = 58) from urban middle schools were randomly assigned to each experimental condition. Results suggested that, in comparison to individual play, competition increased in-game leaming, whereas collaboration decreased performance during the experimental play session. Although out-of-game math fluency improved overall, it did not vary by condition. Furthermore, competition and collaboration elicited greater situational interest and enjoyment and invoked a stronger mastery goal orientation. Additionally, collaboration resulted in stronger intentions to play the game again and to recommend it to others. Results are discussed in terms of the potential for mathematics learning games and technology to increase student leaming and motivation and to demonstrate how different modes of engagement can inform the instructional design of such games.

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