Advanced Interactive TechnologiesOpen OpportunitiesEstimating human poses within global trajectories is critical for applications such as augmented reality and sports analytics, yet it often demands precisely calibrated cameras and significant computational efforts. With advancements in deep learning and pose estimation technologies, various models can be trained using 2D or 3D motion data. However, effectively integrating these models to predict and analyze human movement trajectories in a continuous and dynamic environment remains challenging. This project aims to create a robust system that estimates and predicts human poses accurately, facilitating advancements in dynamic pose analysis and real-world applications. - Information, Computing and Communication Sciences
- ETH Zurich (ETHZ), Lab Practice, Master Thesis, Semester Project
| Latent diffusion models (LDMs) [1] have recently emerged as a powerful tool for high-quality image generation, offering superior scalability and training efficiency compared to pixel-space diffusion models. While the network architectures of LDMs have received significant attention, other design aspects of these models (for example the forward noise schedule and the autoencoder) remain underexplored. This project aims to enhance the characteristics of LDMs, e.g., quality and efficiency, by investigating various design elements of latent diffusion models.
- Information, Computing and Communication Sciences
- ETH Zurich (ETHZ), Master Thesis, Semester Project
| Digital capture of human bodies is a rapidly growing research area in computer vision and computer graphics that puts scenarios such as life-like mixed-reality (MR) virtual-social interactions into reach. Therefore, we offer projects for modeling and capturing humans at the intersection of computer vision, computer graphics, and machine learning. - Computer Graphics, Computer Vision, Virtual Reality and Related Simulation
- Master Thesis, Semester Project
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