Master Thesis

vor 1 Woche

Zurich, Schweiz Egonym Vollzeit

Visual privacy preservation is a rising concern in the online world. Face anonymization in images is among such privacy preservation tasks. For the end users, an ideal anonymization algorithm should provide photo-realistic and quantitatively different face generation without breaking the user experience. This translates to ensuring to preserve the crucial facial attributes, expressions, and head poses while still providing high-quality and visually-distinct anonymization per end user. Similar to the challenges in other face-related visual tasks, e.g. detection and segmentation, face anonymization can suffer from occlusion and partial crops of the face regions due to framing or extreme poses. One way to address this problem is to design 3D-aware face anonymization solutions with the help of state-of-the-art generative AI models.

Tasks

1. Explore the 3D-aware generative AI solutions to be applied on face anonymization task,
2. Develop proof-of-concept codes to run on cloud and mobile/edge devices.

**Requirements**:

- Student in computer science, machine learning or relevant field
- Experienced Python coder with good command of PyTorch/TF, NumPy, OpenCV, etc (2+ yrs)
- Familiarity with modern ML concepts of generative models and image manipulation
- Proficient in English
- Independent worker & team player

**Benefits**:

- Experienced supervision and guidance

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