59 citations · 91 across the 15 of their papers we have counts for
15 papers
TOM-GS: Editable Video Representation via Temporal Opacity Modulation of Static 3D Gaussians
Marek Lisowski, Łukasz Smoliński, Kornel Howil +3
While Implicit Neural Representations (INRs) and dynamic 3D Gaussian Splatting (3DGS) achieve impressive results in video processing, they often fall short of producing representat…
AE-Flow: AutoEncoder Normalizing Flow
Jakub Mosiński, Piotr Biliński, Thomas Merritt +2
Recently normalizing flows have been gaining traction in text-to-speech (TTS) and voice conversion (VC) due to their state-of-the-art (SOTA) performance. Normalizing flows are unsu…
Creating New Voices using Normalizing Flows
Piotr Bilinski, Thomas Merritt, Abdelhamid Ezzerg +5
Creating realistic and natural-sounding synthetic speech remains a big challenge for voice identities unseen during training. As there is growing interest in synthesizing voices of…
Comparing normalizing flows and diffusion models for prosody and acoustic modelling in text-to-speech
Guangyan Zhang, Thomas Merritt, Manuel Sam Ribeiro +10
Neural text-to-speech systems are often optimized on L1/L2 losses, which make strong assumptions about the distributions of the target data space. Aiming to improve those assumptio…
SCRAPS: Speech Contrastive Representations of Acoustic and Phonetic Spaces
Ivan Vallés-Pérez, Grzegorz Beringer, Piotr Bilinski +2
Numerous examples in the literature proved that deep learning models have the ability to work well with multimodal data. Recently, CLIP has enabled deep learning systems to learn s…
ML framework for global river flood predictions based on the Caravan dataset
Ioanna Bouri, Manu Lahariya, Omer Nivron +4
Reliable prediction of river floods in the first 72 hours can reduce harm because emergency agencies have sufficient time to prepare and deploy for help at the scene. Such river fl…