We demonstrate that it is possible to perform face-related computer vision in the wild using synthetic data alone.
Recent work on Neural Radiance Fields (NeRF) showed how neural networks can be used to encode complex 3D environments that can be rendered photorealistically from novel viewpoints.
no code implementations • 22 Dec 2020 • Adam D. Hincks, Simone Aiola, J. Richard Bond, Erminia Calabrese, Andrei Frolov, José Tomás Gálvez Ghersi, Renée Hložek, Matthew Johnson, Mathew S. Madhavacheril, Moritz Münchmeyer, Lyman A. Page, Jonathan Sievers, Suzanne T. Staggs, Alexander van Engelen
Observations of the cosmic microwave background (CMB) are an incredibly fertile source of information for studying the origins and evolution of the Universe.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics
no code implementations • 3 Nov 2020 • Rachel C. Kurchin, Eric Muckley, Lance Kavalsky, Vinay Hegde, Dhairya Gandhi, Xiaoyu Sun, Matthew Johnson, Alan Edelman, James Saal, Christopher Vincent Rackauckas, Bryce Meredig, Viral Shah, Venkatasubramanian Viswanathan
Large-scale electrification is vital to addressing the climate crisis, but many engineering challenges remain to fully electrifying both the chemical industry and transportation.
For the Odd Cycle Transversal problem, the task is to find a small set $S$ of vertices in a graph that intersects every cycle of odd length.
Data Structures and Algorithms Discrete Mathematics Combinatorics
Analysis of faces is one of the core applications of computer vision, with tasks ranging from landmark alignment, head pose estimation, expression recognition, and face recognition among others.
In contrast to computer graphics approaches, generative models learned from more readily available 2D image data have been shown to produce samples of human faces that are hard to distinguish from real data.
Our ability to sample realistic natural images, particularly faces, has advanced by leaps and bounds in recent years, yet our ability to exert fine-tuned control over the generative process has lagged behind.
This paper has been withdrawn by the authors due to insufficient or definition error(s) in the ethics approval protocol.
For both a state-of-the-art VAE on 64x64 ImageNet and Image Transformer on 256x256 CelebA-HQ, our approach achieves an optimal linear speedup from 1 to 256 TPUv2 chips.
Most of the current literature in these fields involve visualizing the time-series for noticeable structure and hard coding them into pre-specified parametric functions.
We propose the segmented iHMM (siHMM), a hierarchical infinite hidden Markov model (iHMM) that supports a simple, efficient inference scheme.
For example, nucleotides in a DNA sequence, children's names in a given state and year, and text documents are all commonly modeled with multinomial distributions.
In this paper, we present an algorithm for optimizing the split functions at all levels of the tree jointly with the leaf parameters, based on a global objective.
Sampling inference methods are computationally difficult to scale for many models in part because global dependencies can reduce opportunities for parallel computation.