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Author: Amogh Subbakrishna Adishesha Publisher: ISBN: Category : Languages : en Pages :
Book Description
Prediction of the human motion model has been an intrinsic part of several applicationsover diverse fields like gaming, augmented reality and cinematic graphics. The ability to estimatemotion, ahead of time, helps robots predict human action and thus reduce time to reacteffectively. In real time applications such as pedestrian motion prediction, the availability of longmotion sequences at test time is rare. In this work, we propose a new architecture to predictivelymodel human motion partially from noise. We utilize the data synthesizing ability of GenerativeAdversarial Networks(GANs) to provide artificial motion frames that help in prediction of themotion sequence in an LSTM-RNN framework. The well proven Recurrent Neural Network isused as a discriminator in training a weaker LSTM generator that we later exploit in creatingground truth like data from randomly sampled frames with mean pose and added noise. Pivotingon the evaluation metrics used in latest works, we discuss the recent motion prediction techniquesand compare the results. We also evaluate the training procedures, input requirements andcomplexity of the structures, thus illustrating the simplicity and accuracy of a GAN based inputreduction model.
Author: Amogh Subbakrishna Adishesha Publisher: ISBN: Category : Languages : en Pages :
Book Description
Prediction of the human motion model has been an intrinsic part of several applicationsover diverse fields like gaming, augmented reality and cinematic graphics. The ability to estimatemotion, ahead of time, helps robots predict human action and thus reduce time to reacteffectively. In real time applications such as pedestrian motion prediction, the availability of longmotion sequences at test time is rare. In this work, we propose a new architecture to predictivelymodel human motion partially from noise. We utilize the data synthesizing ability of GenerativeAdversarial Networks(GANs) to provide artificial motion frames that help in prediction of themotion sequence in an LSTM-RNN framework. The well proven Recurrent Neural Network isused as a discriminator in training a weaker LSTM generator that we later exploit in creatingground truth like data from randomly sampled frames with mean pose and added noise. Pivotingon the evaluation metrics used in latest works, we discuss the recent motion prediction techniquesand compare the results. We also evaluate the training procedures, input requirements andcomplexity of the structures, thus illustrating the simplicity and accuracy of a GAN based inputreduction model.
Author: Adrien Bartoli Publisher: Springer Nature ISBN: 3030670708 Category : Computers Languages : en Pages : 786
Book Description
The 6-volume set, comprising the LNCS books 12535 until 12540, constitutes the refereed proceedings of 28 out of the 45 workshops held at the 16th European Conference on Computer Vision, ECCV 2020. The conference was planned to take place in Glasgow, UK, during August 23-28, 2020, but changed to a virtual format due to the COVID-19 pandemic. The 249 full papers, 18 short papers, and 21 further contributions included in the workshop proceedings were carefully reviewed and selected from a total of 467 submissions. The papers deal with diverse computer vision topics. Part III includes the Advances in Image Manipulation Workshop and Challenges.
Author: Hiroshi Ishikawa Publisher: Springer Nature ISBN: 3030695328 Category : Computers Languages : en Pages : 733
Book Description
The six volume set of LNCS 12622-12627 constitutes the proceedings of the 15th Asian Conference on Computer Vision, ACCV 2020, held in Kyoto, Japan, in November/ December 2020.* The total of 254 contributions was carefully reviewed and selected from 768 submissions during two rounds of reviewing and improvement. The papers focus on the following topics: Part I: 3D computer vision; segmentation and grouping Part II: low-level vision, image processing; motion and tracking Part III: recognition and detection; optimization, statistical methods, and learning; robot vision Part IV: deep learning for computer vision, generative models for computer vision Part V: face, pose, action, and gesture; video analysis and event recognition; biomedical image analysis Part VI: applications of computer vision; vision for X; datasets and performance analysis *The conference was held virtually.
Author: Shai Avidan Publisher: Springer Nature ISBN: 3031200683 Category : Computers Languages : en Pages : 804
Book Description
The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
Author: Wang, Liang Publisher: IGI Global ISBN: 1605669016 Category : Computers Languages : en Pages : 318
Book Description
"This book highlights the development of robust and effective vision-based motion understanding systems, addressing specific vision applications such as surveillance, sport event analysis, healthcare, video conferencing, and motion video indexing and retrieval"--Provided by publisher.
Author: Andrea Vedaldi Publisher: Springer Nature ISBN: 3030584526 Category : Computers Languages : en Pages : 856
Book Description
The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
Author: Arun Solanki Publisher: Academic Press ISBN: 0128236132 Category : Science Languages : en Pages : 444
Book Description
Generative Adversarial Networks (GAN) have started a revolution in Deep Learning, and today GAN is one of the most researched topics in Artificial Intelligence. Generative Adversarial Networks for Image-to-Image Translation provides a comprehensive overview of the GAN (Generative Adversarial Network) concept starting from the original GAN network to various GAN-based systems such as Deep Convolutional GANs (DCGANs), Conditional GANs (cGANs), StackGAN, Wasserstein GANs (WGAN), cyclical GANs, and many more. The book also provides readers with detailed real-world applications and common projects built using the GAN system with respective Python code. A typical GAN system consists of two neural networks, i.e., generator and discriminator. Both of these networks contest with each other, similar to game theory. The generator is responsible for generating quality images that should resemble ground truth, and the discriminator is accountable for identifying whether the generated image is a real image or a fake image generated by the generator. Being one of the unsupervised learning-based architectures, GAN is a preferred method in cases where labeled data is not available. GAN can generate high-quality images, images of human faces developed from several sketches, convert images from one domain to another, enhance images, combine an image with the style of another image, change the appearance of a human face image to show the effects in the progression of aging, generate images from text, and many more applications. GAN is helpful in generating output very close to the output generated by humans in a fraction of second, and it can efficiently produce high-quality music, speech, and images. Introduces the concept of Generative Adversarial Networks (GAN), including the basics of Generative Modelling, Deep Learning, Autoencoders, and advanced topics in GAN Demonstrates GANs for a wide variety of applications, including image generation, Big Data and data analytics, cloud computing, digital transformation, E-Commerce, and Artistic Neural Networks Includes a wide variety of biomedical and scientific applications, including unsupervised learning, natural language processing, pattern recognition, image and video processing, and disease diagnosis Provides a robust set of methods that will help readers to appropriately and judiciously use the suitable GANs for their applications
Author: Mehdi Ghayoumi Publisher: CRC Press ISBN: 1003805493 Category : Computers Languages : en Pages : 671
Book Description
This book is an all-inclusive resource that provides a solid foundation on Generative Adversarial Networks (GAN) methodologies, their application to real-world projects, and their underlying mathematical and theoretical concepts. Key Features: Guides you through the complex world of GANs, demystifying their intricacies Accompanies your learning journey with real-world examples and practical applications Navigates the theory behind GANs, presenting it in an accessible and comprehensive way Simplifies the implementation of GANs using popular deep learning platforms Introduces various GAN architectures, giving readers a broad view of their applications Nurture your knowledge of AI with our comprehensive yet accessible content Practice your skills with numerous case studies and coding examples Reviews advanced GANs, such as DCGAN, cGAN, and CycleGAN, with clear explanations and practical examples Adapts to both beginners and experienced practitioners, with content organized to cater to varying levels of familiarity with GANs Connects the dots between GAN theory and practice, providing a well-rounded understanding of the subject Takes you through GAN applications across different data types, highlighting their versatility Inspires the reader to explore beyond this book, fostering an environment conducive to independent learning and research Closes the gap between complex GAN methodologies and their practical implementation, allowing readers to directly apply their knowledge Empowers you with the skills and knowledge needed to confidently use GANs in your projects Prepare to deep dive into the captivating realm of GANs and experience the power of AI like never before with Generative Adversarial Networks (GANs) in Practice. This book brings together the theory and practical aspects of GANs in a cohesive and accessible manner, making it an essential resource for both beginners and experienced practitioners.
Author: Anand Gopalakrishnan Publisher: ISBN: Category : Languages : en Pages :
Book Description
This work proposes novel neural temporal models for predicting and synthesizing human motion, achieving state-of-the-art in modeling long-term motion trajectories while being competitive with prior work in short-term prediction, with significantly less computational expense. Key aspects of the proposed system include: 1) a novel, two-level processing architecture that helps in generating "guiding" trajectories, 2) a set of easily computable features that incorporate motion derivative information into the model, and 3) a novel multi-objective loss function that helps the model to incrementally progress from the simpler task of next-step prediction to the harder task of multi-step closed-loop prediction. The results demonstrate that these innovations facilitate improved modeling of long-term motion trajectories. Finally, a novel metric, called Normalized Power Spectrum Similarity (NPSS) is proposed, to evaluate the long-term predictive ability of motion synthesis models, complementing the popular mean-squared error (MSE) measure of the Euler jointangles over time. A user study is conducted to determine if the proposed NPSS correlates with human evaluation of long-term motion more strongly than MSE and finds that it indeed does.
Author: Amogh Subbakrishna Adishesha
Author: Amogh Subbakrishna Adishesha
Author: Adrien Bartoli
Author: Hiroshi Ishikawa
Author: Shai Avidan
Author: Wang, Liang
Author: Andrea Vedaldi
Author: Arun Solanki
Author: Mehdi Ghayoumi
Author: Joaquim Filipe
Author: Anand Gopalakrishnan