Name: Shivangi Aneja
Position: Ph.D Candidate
Phone: +49-89-289-18489
Room No: 02.07.041


Shivangi Aneja is a PhD student in Visual Computing Lab advised by Prof. Matthias Nießner. Prior to that, she completed her Master’s degree in Informatics from Technical University of Munich. She joined the lab during her master thesis, where she worked on “Generalized Zero and Few-Shot Transfer for Facial Forgery Detection”. She holds a Bachelor’s degree in Computer Science from the National Institute of Technology, India, where she graduated with a Gold Medal for academic excellence.

Research Interest

Computer Vision, Transfer Learning



FaceTalk: Audio-Driven Motion Diffusion for Neural Parametric Head Models
Shivangi Aneja, Justus Thies, Angela Dai, Matthias Nießner
CVPR 2024
Given an input audio signal, FaceTalk proposes a diffusion-based approach to synthesize high-quality and temporally consistent 3D motion sequences of high-fidelity human heads as neural parametric head models. FaceTalk can generate detailed facial expressions including wrinkles and eye blinks alongside temporally synchronized mouth movements for diverse audio inputs including songs and foreign languages.
[video][bibtex][project page]


ClipFace: Text-guided Editing of Textured 3D Morphable Models
Shivangi Aneja, Justus Thies, Angela Dai, Matthias Nießner
ClipFace learns a self-supervised generative model for jointly synthesizing geometry and texture leveraging 3D morphable face models, that can be guided by text prompts. For a given 3D mesh with fixed topology, we can generate arbitrary face textures as UV maps. The textured mesh can then be manipulated with text guidance to generate diverse set of textures and geometric expressions in 3D by altering (a) only the UV texture maps for Texture Manipulation and (b) both UV maps and mesh geometry for Expression Manipulation.
[video][bibtex][project page]

COSMOS: Catching Out-of-Context Misinformation with Self-Supervised Learning
Shivangi Aneja, Chris Bregler, Matthias Nießner
AAAI 2023
Despite the recent attention to DeepFakes, one of the most prevalent ways to mislead audiences on social media is the use of unaltered images in a new but false context. To address these challenges and support fact-checkers, we propose a new method that automatically detects out-of-context image and text pairs. Our key insight is to leverage grounding of image with text to distinguish out-of-context scenarios that cannot be disambiguated with language alone. Check out the paper for more details.
[video][code][bibtex][project page]


TAFIM: Targeted Adversarial Attacks against Facial Image Manipulations
Shivangi Aneja, Lev Markhasin, Matthias Nießner
ECCV 2022
We introduce a novel data-driven approach that produces image-specific perturbations which are embedded in the original images to prevent face manipulation by causing the manipulation model to produce a predefined manipulation target. Compared to traditional adversarial attack baselines that optimize noise patterns for each image individually, our generalized model only needs a single forward pass, thus running orders of magnitude faster and allowing for easy integration in image processing stacks, even on resource-constrained devices like smartphones. Check out the paper for more details.
[video][code][bibtex][project page]