References

http://cs.stanford.edu/people/karpathy/deepimagesent/

http://www.ee.cuhk.edu.hk/~xgwang/papers/ouyangWiccv13.pdf Joint Deep Learning for Pedestrian Detection

This paper proposes that they should be jointly learned in order to maximize their strengths through cooperation. We formulate these four components into a joint deep learning framework and propose a new deep network architecture.

http://homepages.inf.ed.ac.uk/tkomura/dog.pdf Mode-Adaptive Neural Networks for Quadruped Motion Control

. The system is composed of the motion prediction network and the gating network. At each frame, the motion prediction network computes the character state in the current frame given the state in the previous frame and the user-provided control signals. The gating network dynamically updates the weights of the motion prediction network by selecting and blending what we call the expert weights, each of which specializes in a particular movement. Due to the increased flexibility, the system can learn consistent expert weights across a wide range of non-periodic/periodic actions, from unstructured motion capture data, in an end-to-end fashion. In addition, the users are released from performing complex labeling of phases in different gaits. We show that this architecture is suitable for encoding the multimodality of quadruped locomotion and synthesizing responsive motion in real-time.