no code implementations • ECCV 2020 • Dipanjan Das, Sandika Biswas, Sanjana Sinha, Brojeshwar Bhowmick
Current state-of-the-art methods fail to generate realistic animation from any speech on unknown faces due to their poor gen-eralization over different facial characteristics, languages, and accents.
no code implementations • 17 Aug 2023 • Sanchar Palit, Sandika Biswas
To this end, we propose a continual learning-based 3D reconstruction method where our goal is to design a model using Variational Priors that can still reconstruct the previously seen classes reasonably even after training on new classes.
no code implementations • 27 Jul 2023 • Sandika Biswas, Kejie Li, Biplab Banerjee, Subhasis Chaudhuri, Hamid Rezatofighi
This paper proposes using an implicit feature representation of the scene elements to distinguish a physically plausible alignment of humans and objects from an implausible one.
no code implementations • 2 May 2022 • Sanjana Sinha, Sandika Biswas, Ravindra Yadav, Brojeshwar Bhowmick
We propose a graph convolutional neural network that uses speech content feature, along with an independent emotion input to generate emotion and speech-induced motion on facial geometry-aware landmark representation.
no code implementations • 25 May 2020 • Sanjana Sinha, Sandika Biswas, Brojeshwar Bhowmick
The necessary attributes of having a realistic face animation are 1) audio-visual synchronization (2) identity preservation of the target individual (3) plausible mouth movements (4) presence of natural eye blinks.
no code implementations • arXiv 2019 • Sandika Biswas, Sanjana Sinha, Kavya Gupta and Brojeshwar Bhowmick
Our method uses re-projection error minimizationas a constraint to predict the 3d locations of body joints, andthis is crucial for training on data where the 3d ground-truth isnot present.
no code implementations • 3 May 2019 • Sandika Biswas, Sanjana Sinha, Kavya Gupta, Brojeshwar Bhowmick
Few approaches have utilized training images from both 3d and 2d pose datasets in a weakly-supervised manner for learning 3d poses in unconstrained settings.