no code implementations • 31 Jan 2024 • Yue Zhang, Ben Colman, Ali Shahriyari, Gaurav Bharaj
State-of-the-art approaches rely on image-based features extracted via neural networks for the deepfake detection binary classification.
no code implementations • 24 Jan 2024 • Miao Zhang, Zee Fryer, Ben Colman, Ali Shahriyari, Gaurav Bharaj
Machine learning model bias can arise from dataset composition: sensitive features correlated to the learning target disturb the model decision rule and lead to performance differences along the features.
no code implementations • CVPR 2023 • Chuhan Chen, Matthew O'Toole, Gaurav Bharaj, Pablo Garrido
We build on part-based implicit shape models that decompose a global deformation field into local ones.
no code implementations • CVPR 2023 • Xingzhe He, Gaurav Bharaj, David Ferman, Helge Rhodin, Pablo Garrido
Supervised keypoint localization methods rely on large manually labeled image datasets, where objects can deform, articulate, or occlude.
no code implementations • ICCV 2023 • Berkay Kicanaoglu, Pablo Garrido, Gaurav Bharaj
Such representations along with 3D tracking can be used as self-supervision to train a generator with control over coarse expressions and finer facial attributes.
no code implementations • 19 Mar 2022 • Nisarg A. Shah, Gaurav Bharaj
We present a novel algorithm to reduce tensor compute required by a conditional image generation autoencoder without sacrificing quality of photo-realistic image generation.
no code implementations • 19 Mar 2022 • David Ferman, Gaurav Bharaj
Training a small dataset alongside a large(r) dataset helps with robust learning for the former, and provides a universal mechanism for facial landmark localization for new and/or smaller standard datasets.
no code implementations • 8 Apr 2021 • David Ferman, Gaurav Bharaj
We propose a general purpose approach to detect landmarks with improved temporal consistency, and personalization.
no code implementations • 8 Apr 2021 • Eric Engelhart, Mahsa Elyasi, Gaurav Bharaj
And transformer-based models require significant training data, and do not generalize well, especially for dialects with limited data.
no code implementations • 8 Apr 2021 • Mahsa Elyasi, Gaurav Bharaj
In this work, we propose a novel carefully designed strategy for conditioning Tacotron-2 on two fundamental prosodic features in English -- stress syllable and pitch accent, that help achieve more natural prosody.
1 code implementation • 13 Jan 2021 • Abdallah Dib, Gaurav Bharaj, Junghyun Ahn, Cédric Thébault, Philippe-Henri Gosselin, Marco Romeo, Louis Chevallier
The proposed method models scene illumination via a novel, parameterized virtual light stage, which in-conjunction with differentiable ray-tracing, introduces a coarse-to-fine optimization formulation for face reconstruction.
no code implementations • CVPR 2020 • Ayush Tewari, Mohamed Elgharib, Gaurav Bharaj, Florian Bernard, Hans-Peter Seidel, Patrick Pérez, Michael Zollhöfer, Christian Theobalt
StyleGAN generates photorealistic portrait images of faces with eyes, teeth, hair and context (neck, shoulders, background), but lacks a rig-like control over semantic face parameters that are interpretable in 3D, such as face pose, expressions, and scene illumination.
no code implementations • 3 Oct 2019 • Abdallah Dib, Gaurav Bharaj, Junghyun Ahn, Cedric Thebault, Philippe-Henri Gosselin, Louis Chevallier
We present a novel strategy to automatically reconstruct 3D faces from monocular images with explicitly disentangled facial geometry (pose, identity and expression), reflectance (diffuse and specular albedo), and self-shadows.
no code implementations • CVPR 2019 • Ayush Tewari, Florian Bernard, Pablo Garrido, Gaurav Bharaj, Mohamed Elgharib, Hans-Peter Seidel, Patrick Pérez, Michael Zollhöfer, Christian Theobalt
In contrast, we propose multi-frame video-based self-supervised training of a deep network that (i) learns a face identity model both in shape and appearance while (ii) jointly learning to reconstruct 3D faces.