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D6
Visual Computing and Artificial Intelligence

Research Interests

My research specializes in developing neural network architectures for 3D motion analysis, modeling, and synthesis. My work involves leveraging advanced deep learning models, including transformers, diffusion models, and large visual-language models, for applications in motion-conditioned synthesis and multi-modal generation. I am passionate about applying these techniques to solve complex problems in computer vision and AI.

Publications


ReMoS: 3D Motion-Conditioned Reaction Synthesis for Two-Person Interactions
Anindita Ghosh, Rishabh Dabral, Vladislav Golyanik, Christian Theobalt, Philipp Slusallek
European Conference on Computer Vision (ECCV), 2024

[Project page]

IMoS: Intent-Driven Full-Body Motion Synthesis for Human-Object Interactions
Anindita Ghosh, Rishabh Dabral, Vladislav Golyanik, Christian Theobalt, Philipp Slusallek
EUROGRAPHICS Computer Graphics Forum 42 (2), 1-12, 2023
Synthesis of Compositional Animations from Textual Descriptions
Anindita Ghosh, Noshaba Cheema, Cennet Oguz, Christian Theobalt, Philipp Slusallek
International Conference on Computer Vision (ICCV) 2021.

[Paper] [Poster]

Also accepted as a poster:
"Text-Based Motion Synthesis with a Hierarchical Two-Stream RNN". ACM SIGGRAPH 2021. [Poster]

Recent Positions

Education

Invited Talks