1 code implementation • 24 Apr 2023 • Aashish Rai, Hiresh Gupta, Ayush Pandey, Francisco Vicente Carrasco, Shingo Jason Takagi, Amaury Aubel, Daeil Kim, Aayush Prakash, Fernando de la Torre
By combining 2D face generative models with semantic face manipulation, this method enables editing of detailed 3D rendered faces.
Ranked #5 on 3D Face Reconstruction on REALY (side-view)
1 code implementation • 6 Sep 2022 • Justin Theiss, Jay Leverett, Daeil Kim, Aayush Prakash
Image-to-image translation has played an important role in enabling synthetic data for computer vision.
1 code implementation • 30 Aug 2022 • Fariborz Taherkhani, Aashish Rai, Quankai Gao, Shaunak Srivastava, Xuanbai Chen, Fernando de la Torre, Steven Song, Aayush Prakash, Daeil Kim
3D face modeling has been an active area of research in computer vision and computer graphics, fueling applications ranging from facial expression transfer in virtual avatars to synthetic data generation.
no code implementations • ICCV 2021 • Siva Karthik Mustikovela, Shalini De Mello, Aayush Prakash, Umar Iqbal, Sifei Liu, Thu Nguyen-Phuoc, Carsten Rother, Jan Kautz
We present SSOD, the first end-to-end analysis-by synthesis framework with controllable GANs for the task of self-supervised object detection.
no code implementations • ICCV 2021 • Aayush Prakash, Shoubhik Debnath, Jean-Francois Lafleche, Eric Cameracci, Gavriel State, Stan Birchfield, Marc T. Law
Synthetic data is emerging as a promising solution to the scalability issue of supervised deep learning, especially when real data are difficult to acquire or hard to annotate.
no code implementations • 28 Sep 2020 • Aayush Prakash, Shoubhik Debnath, Jean Francois Lafleche, Eric Cameracci, Gavriel State, Marc T Law
However, neural network models trained on synthetic data, do not perform well on real data because of the domain gap.
no code implementations • ICCV 2019 • Amlan Kar, Aayush Prakash, Ming-Yu Liu, Eric Cameracci, Justin Yuan, Matt Rusiniak, David Acuna, Antonio Torralba, Sanja Fidler
Training models to high-end performance requires availability of large labeled datasets, which are expensive to get.
no code implementations • 23 Oct 2018 • Aayush Prakash, Shaad Boochoon, Mark Brophy, David Acuna, Eric Cameracci, Gavriel State, Omer Shapira, Stan Birchfield
Moreover, synthetic SDR data combined with real KITTI data outperforms real KITTI data alone.
1 code implementation • 18 Apr 2018 • Jonathan Tremblay, Aayush Prakash, David Acuna, Mark Brophy, Varun Jampani, Cem Anil, Thang To, Eric Cameracci, Shaad Boochoon, Stan Birchfield
We present a system for training deep neural networks for object detection using synthetic images.