14,956 research outputs found

    Speech-driven Animation with Meaningful Behaviors

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    Conversational agents (CAs) play an important role in human computer interaction. Creating believable movements for CAs is challenging, since the movements have to be meaningful and natural, reflecting the coupling between gestures and speech. Studies in the past have mainly relied on rule-based or data-driven approaches. Rule-based methods focus on creating meaningful behaviors conveying the underlying message, but the gestures cannot be easily synchronized with speech. Data-driven approaches, especially speech-driven models, can capture the relationship between speech and gestures. However, they create behaviors disregarding the meaning of the message. This study proposes to bridge the gap between these two approaches overcoming their limitations. The approach builds a dynamic Bayesian network (DBN), where a discrete variable is added to constrain the behaviors on the underlying constraint. The study implements and evaluates the approach with two constraints: discourse functions and prototypical behaviors. By constraining on the discourse functions (e.g., questions), the model learns the characteristic behaviors associated with a given discourse class learning the rules from the data. By constraining on prototypical behaviors (e.g., head nods), the approach can be embedded in a rule-based system as a behavior realizer creating trajectories that are timely synchronized with speech. The study proposes a DBN structure and a training approach that (1) models the cause-effect relationship between the constraint and the gestures, (2) initializes the state configuration models increasing the range of the generated behaviors, and (3) captures the differences in the behaviors across constraints by enforcing sparse transitions between shared and exclusive states per constraint. Objective and subjective evaluations demonstrate the benefits of the proposed approach over an unconstrained model.Comment: 13 pages, 12 figures, 5 table

    Human Motion Retrieval Using Video or Drawn Sketch

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    The importance of motion retrieval is increasing now a days. The majority of existing motion retrieval labor intensive, there has been a recent paradigm move in the animation industry with an increasing use of pre-recorded movement of animating exclusive figures. An essential need to use motion catch data is an efficient method for listing and accessing movements. I n this work, a novel sketching interface for interpreting the problem is provided. This simple strategy allows the user to determine the necessary movement by drawing several movement swings over a attracted personality, which needs less effort and extends the users expressiveness. To support the real-time interface, a specific development of the movements and the hand-drawn question is needed. Here we are implementing the Conjugate Gradient method for retrieving motion from hand drawn sketch and video. It is an optimization and prominent iterative method. It is fast and uses a small amount of storage

    Implantation of subcutaneous heart rate data loggers in southern elephant seals (Mirounga leonina)

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    Unlike most phocid species (Phocidae), Mirounga leonina (southern elephant seals) experience a catastrophic moult where they not only replace their hair but also their epidermis when ashore for approximately 1 month. Few studies have investigated behavioural and physiological adaptations of southern elephant seals during the moult fast, a particularly energetically costly life cycle’s phase. Recording heart rate is a reliable technique for estimating energy expenditure in the field. For the first time, subcutaneous heart rate data loggers were successfully implanted during the moult in two free-ranging southern elephant seals over 3–6 days. No substantial postoperative complications were encountered and consistent heart rate data were obtained. This promising surgical technique opens new opportunities for monitoring heart rate in phocid seals

    Structure from Recurrent Motion: From Rigidity to Recurrency

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    This paper proposes a new method for Non-Rigid Structure-from-Motion (NRSfM) from a long monocular video sequence observing a non-rigid object performing recurrent and possibly repetitive dynamic action. Departing from the traditional idea of using linear low-order or lowrank shape model for the task of NRSfM, our method exploits the property of shape recurrency (i.e., many deforming shapes tend to repeat themselves in time). We show that recurrency is in fact a generalized rigidity. Based on this, we reduce NRSfM problems to rigid ones provided that certain recurrency condition is satisfied. Given such a reduction, standard rigid-SfM techniques are directly applicable (without any change) to the reconstruction of non-rigid dynamic shapes. To implement this idea as a practical approach, this paper develops efficient algorithms for automatic recurrency detection, as well as camera view clustering via a rigidity-check. Experiments on both simulated sequences and real data demonstrate the effectiveness of the method. Since this paper offers a novel perspective on rethinking structure-from-motion, we hope it will inspire other new problems in the field.Comment: To appear in CVPR 201

    Retrieving, annotating and recognizing human activities in web videos

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    Recent e orts in computer vision tackle the problem of human activity understanding in video sequences. Traditionally, these algorithms require annotated video data to learn models. In this work, we introduce a novel data collection framework, to take advantage of the large amount of video data available on the web. We use this new framework to retrieve videos of human activities, and build training and evaluation datasets for computer vision algorithms. We rely on Amazon Mechanical Turk workers to obtain high accuracy annotations. An agglomerative clustering technique brings the possibility to achieve reliable and consistent annotations for temporal localization of human activities in videos. Using two datasets, Olympics Sports and our novel Daily Human Activities dataset, we show that our collection/annotation framework can make robust annotations of human activities in large amount of video data
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