4 papers
Mem-MLP: Real-Time 3D Human Motion Generation from Sparse Inputs
Sinan Mutlu, Georgios F. Angelis, Savas Ozkan +3
Realistic and smooth full-body tracking is crucial for immersive AR/VR applications. Existing systems primarily track head and hands via Head Mounted Devices (HMDs) and controllers…
Retrieval Augmented Generation based context discovery for ASR
Dimitrios Siskos, Stavros Papadopoulos, Pablo Peso Parada +3
This work investigates retrieval augmented generation as an efficient strategy for automatic context discovery in context-aware Automatic Speech Recognition (ASR) system, in order…
Efficient 3D Full-Body Motion Generation from Sparse Tracking Inputs with Temporal Windows
Georgios Fotios Angelis, Savas Ozkan, Sinan Mutlu +3
To have a seamless user experience on immersive AR/VR applications, the importance of efficient and effective Neural Network (NN) models is undeniable, since missing body parts tha…
persoDA: Personalized Data Augmentation for Personalized ASR
Pablo Peso Parada, Spyros Fontalis, Md Asif Jalal +6
Data augmentation (DA) is ubiquitously used in training of Automatic Speech Recognition (ASR) models. DA offers increased data variability, robustness and generalization against di…