no code implementations • 3 Feb 2024 • Partha Ghosh, Joy Sharma, Nilesh Pandey
Cloud computing has high applicability as an Internet based service that relies on sharing computing resources.
no code implementations • 11 Jan 2024 • Partha Ghosh, Soubhik Sanyal, Cordelia Schmid, Bernhard Schölkopf
To capture these dependencies, our approach incorporates a hybrid explicit-implicit tri-plane representation inspired by 3D-aware generative frameworks developed for three-dimensional object representation and employs a singular latent code to model an entire video sequence.
no code implementations • 21 Aug 2023 • Soubhik Sanyal, Partha Ghosh, Jinlong Yang, Michael J. Black, Justus Thies, Timo Bolkart
We use intermediate activations of the learned geometry model to condition our texture generator.
no code implementations • 19 Jul 2023 • Omri Ben-Dov, Pravir Singh Gupta, Victoria Abrevaya, Michael J. Black, Partha Ghosh
Generative Adversarial Networks (GANs) can produce high-quality samples, but do not provide an estimate of the probability density around the samples.
no code implementations • 24 Oct 2022 • Ahnaf Mozib Samin, M. Humayon Kobir, Md. Mushtaq Shahriyar Rafee, M. Firoz Ahmed, Mehedi Hasan, Partha Ghosh, Shafkat Kibria, M. Shahidur Rahman
We also demonstrate the significance of domain selection while building a corpus by assessing these models on a novel multi-domain Bangladeshi Bangla ASR evaluation benchmark - BanSpeech, which contains approximately 6. 52 hours of human-annotated speech and 8085 utterances from 13 distinct domains.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 26 Jun 2022 • Partha Ghosh
Reconstructing 3D non-watertight mesh from an unoriented point cloud is an unexplored area in computer vision and computer graphics.
no code implementations • 23 Jun 2022 • Omri Ben-Dov, Pravir Singh Gupta, Victoria Fernandez Abrevaya, Michael J. Black, Partha Ghosh
Modern generative models are roughly divided into two main categories: (1) models that can produce high-quality random samples, but cannot estimate the exact density of new data points and (2) those that provide exact density estimation, at the expense of sample quality and compactness of the latent space.
no code implementations • 8 Dec 2021 • Partha Ghosh, Dominik Zietlow, Michael J. Black, Larry S. Davis, Xiaochen Hu
Our \textbf{InvGAN}, short for Invertible GAN, successfully embeds real images to the latent space of a high quality generative model.
1 code implementation • CVPR 2021 • Mohamed Hassan, Partha Ghosh, Joachim Tesch, Dimitrios Tzionas, Michael J. Black
Second, we show that POSA's learned representation of body-scene interaction supports monocular human pose estimation that is consistent with a 3D scene, improving on the state of the art.
Ranked #4 on Contact Detection on BEHAVE
1 code implementation • 31 Aug 2020 • Partha Ghosh, Pravir Singh Gupta, Roy Uziel, Anurag Ranjan, Michael Black, Timo Bolkart
Specifically, we condition StyleGAN2 on FLAME, a generative 3D face model.
4 code implementations • ICLR 2020 • Partha Ghosh, Mehdi S. M. Sajjadi, Antonio Vergari, Michael Black, Bernhard Schölkopf
Variational Autoencoders (VAEs) provide a theoretically-backed and popular framework for deep generative models.
no code implementations • 31 May 2018 • Partha Ghosh, Arpan Losalka, Michael J. Black
Our model has the form of a variational autoencoder, with a Gaussian mixture prior on the latent vector.
no code implementations • 10 Apr 2017 • Partha Ghosh, Jie Song, Emre Aksan, Otmar Hilliges
Furthermore, we propose new evaluation protocols to assess the quality of synthetic motion sequences even for which no ground truth data exists.