Human Centric Synthetic Data

Introduction to Synthetic Human Data and Generative AI: Introduction to synthetic human data and its role in modern artificial intelligence. Motivation for synthetic data generation, limitations of real-world datasets, including privacy concerns, demographic imbalance, annotation costs, and data scarcity. Applications, opportunities, and challenges of synthetic human data in computer vision, and human-centered AI. Preliminaries of Neural Networks: Fundamentals of deep neural networks, including basic architectures, optimization, regularization, and learning principles that form the foundation of modern computer vision and generative AI. Visual Representation Learning for Human Data: Principles of visual representation learning for human-centered applications. Deep neural network architectures, feature embeddings, metric learning, loss functions, and modern approaches for human recognition. Foundations of Generative Models: Fundamental concepts of deep generative models for synthetic human data generation, including probabilistic generative modeling, latent variable models, Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs). Advanced Generative Models: Recent advances in generative AI with a focus on controllable data generation. Conditional generative models, diffusion models, conditioning mechanisms, and identity- or attribute-controlled human data synthesis. Synthetic Human Data for Model Development: Generation and utilization of synthetic human data for developing machine learning models. Synthetic data augmentation, synthetic-to-real transfer, domain adaptation, and applications to human recognition and related computer vision tasks. Foundation Models for Human Analysis: Foundation models and vision-language models for human-centered analysis. Large pretrained models, multimodal learning, transfer learning, parameter-efficient fine-tuning, and adaptation to downstream human analysis tasks. Deepfake Generation and Detection: Principles of deepfake generation and detection. Modern generative models for creating realistic manipulated human images and videos, state-of-the-art deepfake detection techniques, evaluation methodologies, and challenges related to security, privacy, and responsible AI. Synthetic Behavioral Data: Generation of synthetic behavioral data for human-centered applications. Topics include gait synthesis, gesture and body movement generation, temporal generative models, behavioral data augmentation, privacy-preserving behavioral datasets, and the use of synthetic behavioral data for recognition, authentication, and activity analysis. Synthetic Data Evaluation: Evaluation methodologies for synthetic human data, including data quality, realism, diversity, identity preservation, and downstream task performance. Quantitative and qualitative evaluation metrics for assessing the utility of synthetic data in machine learning applications.