In this paper, we present an interactive system to transfer and concatenate motions in different articulated figures, such as human and dog. This system first constructs a union skeleton that contains both bone structures with bone correspondence manually assigned by users, then aligns the initial

are discussed from an algorithm design perspective. The focus is on the major issues such as fitness assignment, diversity preservation, and elitism in general rather than on particular algorithms. Different techniques to implement these strongly related concepts will be discussed, and further important aspects

needing one order of magnitude less training examples. Finally, we present results for articulated objects, which show that the proposed method can categorize and segment unfamiliar objects in different articulations and with widely varying texture patterns, even under significant partial occlusion.

columns. This continuous mapping may be used for various computations, such as production of the image of the object at different viewing angles, illumination conditions. and articulation poses.

Without any explicit knowledge, human infants ac-quire the phonemes of adults who have different artic-ulation parameters. By building a robots that repro-duce this developmental process, we expect to model as yet unknown cognitive developmental processes and to discover fundamental design

Non-rigid object detection and articulated pose estimation are two related and challenging problems in computer vision. Numerous models have been proposed over the years and often address different special cases, such as pedestrian detection or upper body pose estimation in TV footage. This paper

signal, and apply it to voice puppetry: Generating full facial animation from expressive information in an audio track. The voice puppet learns a facial control model from computer vision of real facial behavior, automatically incorporating vocal and facial dynamics such as co-articulation. Animation

The authors offer a framework for conceptualizing collective identity that aims to clarify and make distinctions among dimensions of identification that have not always been clearly articulated. Elements of collective identification included in this framework are self-categorization, evaluation

-organized as experts on multiple levels in order to account for the different categories of sensory-motor flow which the robot experiences. Autonomous switching of activated modules in the lower level actually represents the articulation of the sensory-motor flow. In the meanwhile, a set of RNNs in the higher level

, the articulation points between each pair of rigid parts are obtained by solving a simple motion constraint between the connected parts. To validate our algorithm, we first apply it to segment the different body parts and estimate the joint positions of a person. The acquired kinematic (shape and joint