Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Prakash, Aayush"'
Autor:
Kwon, Youngjoong, Fang, Baole, Lu, Yixing, Dong, Haoye, Zhang, Cheng, Carrasco, Francisco Vicente, Mosella-Montoro, Albert, Xu, Jianjin, Takagi, Shingo, Kim, Daeil, Prakash, Aayush, De la Torre, Fernando
Recent progress in neural rendering has brought forth pioneering methods, such as NeRF and Gaussian Splatting, which revolutionize view rendering across various domains like AR/VR, gaming, and content creation. While these methods excel at interpolat
Externí odkaz:
http://arxiv.org/abs/2407.12777
Autor:
Rai, Aashish, Gupta, Hiresh, Pandey, Ayush, Carrasco, Francisco Vicente, Takagi, Shingo Jason, Aubel, Amaury, Kim, Daeil, Prakash, Aayush, de la Torre, Fernando
In recent years, there has been significant progress in 2D generative face models fueled by applications such as animation, synthetic data generation, and digital avatars. However, due to the absence of 3D information, these 2D models often struggle
Externí odkaz:
http://arxiv.org/abs/2304.12483
Image-to-image translation has played an important role in enabling synthetic data for computer vision. However, if the source and target domains have a large semantic mismatch, existing techniques often suffer from source content corruption aka sema
Externí odkaz:
http://arxiv.org/abs/2209.02686
Autor:
Taherkhani, Fariborz, Rai, Aashish, Gao, Quankai, Srivastava, Shaunak, Chen, Xuanbai, de la Torre, Fernando, Song, Steven, Prakash, Aayush, Kim, Daeil
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. Existing 3D deep learning generative models (
Externí odkaz:
http://arxiv.org/abs/2208.14263
Autor:
Mustikovela, Siva Karthik, De Mello, Shalini, Prakash, Aayush, Iqbal, Umar, Liu, Sifei, Nguyen-Phuoc, Thu, Rother, Carsten, Kautz, Jan
We present SSOD, the first end-to-end analysis-by synthesis framework with controllable GANs for the task of self-supervised object detection. We use collections of real world images without bounding box annotations to learn to synthesize and detect
Externí odkaz:
http://arxiv.org/abs/2110.09848
Autor:
Prakash, Aayush, Debnath, Shoubhik, Lafleche, Jean-Francois, Cameracci, Eric, State, Gavriel, Birchfield, Stan, Law, Marc T.
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. Synthetic data generation, however, can itself be prohibitively expensive
Externí odkaz:
http://arxiv.org/abs/2011.14488
Autor:
Kar, Amlan, Prakash, Aayush, Liu, Ming-Yu, Cameracci, Eric, Yuan, Justin, Rusiniak, Matt, Acuna, David, Torralba, Antonio, Fidler, Sanja
Training models to high-end performance requires availability of large labeled datasets, which are expensive to get. The goal of our work is to automatically synthesize labeled datasets that are relevant for a downstream task. We propose Meta-Sim, wh
Externí odkaz:
http://arxiv.org/abs/1904.11621
Autor:
Prakash, Aayush, Boochoon, Shaad, Brophy, Mark, Acuna, David, Cameracci, Eric, State, Gavriel, Shapira, Omer, Birchfield, Stan
We present structured domain randomization (SDR), a variant of domain randomization (DR) that takes into account the structure and context of the scene. In contrast to DR, which places objects and distractors randomly according to a uniform probabili
Externí odkaz:
http://arxiv.org/abs/1810.10093
Autor:
Tremblay, Jonathan, Prakash, Aayush, Acuna, David, Brophy, Mark, Jampani, Varun, Anil, Cem, To, Thang, Cameracci, Eric, Boochoon, Shaad, Birchfield, Stan
We present a system for training deep neural networks for object detection using synthetic images. To handle the variability in real-world data, the system relies upon the technique of domain randomization, in which the parameters of the simulator$-$
Externí odkaz:
http://arxiv.org/abs/1804.06516
Autor:
Prakash, Aayush
This work presents a static instruction allocation scheme for the precision timed architecture’s (PRET) scratchpad memory. Since PRET provides timing instructions to control the temporal execution of programs, the objective of the allocation scheme
Externí odkaz:
http://hdl.handle.net/10012/7159