7 citations · 11 across the 8 of their papers we have counts for
10 papers
Multi-view Image Diffusion via Coordinate Noise and Fourier Attention
Justin Theiss, Norman Müller, Daeil Kim +1
Recently, text-to-image generation with diffusion models has made significant advancements in both higher fidelity and generalization capabilities compared to previous baselines. H…
Generalizable Human Gaussians for Sparse View Synthesis
Youngjoong Kwon, Baole Fang, Yixing Lu +9
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,…
Towards Realistic Generative 3D Face Models
Aashish Rai, Hiresh Gupta, Ayush Pandey +6
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,…
Unpaired Image Translation via Vector Symbolic Architectures
Justin Theiss, Jay Leverett, Daeil Kim +1
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, ex…
Controllable 3D Generative Adversarial Face Model via Disentangling Shape and Appearance
Fariborz Taherkhani, Aashish Rai, Quankai Gao +6
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 sy…
Self-Supervised Object Detection via Generative Image Synthesis
Siva Karthik Mustikovela, Shalini De Mello, Aayush Prakash +5
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 imag…
Self-Supervised Real-to-Sim Scene Generation
Aayush Prakash, Shoubhik Debnath, Jean-Francois Lafleche +4
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. Syn…
Meta-Sim: Learning to Generate Synthetic Datasets
Amlan Kar, Aayush Prakash, Ming-Yu Liu +6
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 dat…
Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data
Aayush Prakash, Shaad Boochoon, Mark Brophy +5
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 pl…
Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization
Jonathan Tremblay, Aayush Prakash, David Acuna +7
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 techniqu…