Noshaba Cheema

  • Max Planck Institute for Informatics, Saarbrücken, Germany
  • German Research Centre for Artificial Intelligence (DFKI), Saarbrücken, Germany

According to our database1, Noshaba Cheema authored at least 11 papers between 2018 and 2021.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.



In proceedings 
PhD thesis 


Online presence:



Synthesis of Compositional Animations from Textual Descriptions.
CoRR, 2021

Embodied online dance learning objectives of CAROUSEL+.
Proceedings of the IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, 2021

Text-Based Motion Synthesis with a Hierarchical Two-Stream RNN.
Proceedings of the SIGGRAPH 2021: Special Interest Group on Computer Graphics and Interactive Techniques Conference, 2021

A Comparative Study of PnP and Learning Approaches to Super-Resolution in a Real-World Setting.
Proceedings of the Pattern Recognition - 43rd DAGM German Conference, DAGM GCPR 2021, Bonn, Germany, September 28, 2021

Predicting Mid-Air Interaction Movements and Fatigue Using Deep Reinforcement Learning.
Proceedings of the CHI '20: CHI Conference on Human Factors in Computing Systems, 2020

Adaptive gaussian mixture trajectory model for physical model control using motion capture data.
Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, 2019

Learning a Continuous Control of Motion Style from Natural Examples.
Proceedings of the Motion, Interaction and Games, 2019

Stylistic Locomotion Modeling with Conditional Variational Autoencoder.
Proceedings of the 40th Annual Conference of the European Association for Computer Graphics, 2019

Fine-Grained Semantic Segmentation of Motion Capture Data using Dilated Temporal Fully-Convolutional Networks.
Proceedings of the 40th Annual Conference of the European Association for Computer Graphics, 2019

Motion Data and Model Management for Applied Statistical Motion Synthesis.
Proceedings of the Italian Chapter Conference 2019, 2019

Dilated Temporal Fully-Convolutional Network for Semantic Segmentation of Motion Capture Data.
Proceedings of the Poster Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 2018