Extracting Triangular 3D Models, Materials, and Lighting From Images
We learn topology, materials, and environment map lighting jointly from 2D supervision. We directly optimize topology of a triangle mesh, learn materials through volumetric texturing, and leverage differentiable split sum environment lighting. Our output representation is a triangle mesh with spatially varying 2D textures and a high dynamic range environment map, which can be used unmodified in st
CNN-generated images are surprisingly easy to spot... for now
In this work we ask whether it is possible to create a "universal" detector for telling apart real images from these generated by a CNN, regardless of architecture or dataset used. To test this, we collect a dataset consisting of fake images generated by 11 different CNN-based image generator models, chosen to span the space of commonly used architectures today (ProGAN, StyleGAN, BigGAN, CycleGAN,
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USC Iris Computer Vision Lab – USC Institute of Robotics and Intelligent Systems
Lab Introduction IRIS computer vision lab is a unit of USC’s School of Engineering. It was founded in 1986 and has been a major center of government- and industry-sponsored research in computer vision and machine learning. The lab has been active in a number of research topics including object detection and recognition, face identification, 3-D modeling from a sequence of images, activity recognit
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Identifying Players in Broadcast Sports Videos using Conditional Random Fields -- Wei-Lwun Lu
CVPR 2013 Open Access Repository
Anton Milan - Joint Tracking and Segmentation
CRCV | Center for Research in Computer Vision at the University of Central Florida
https://www.robots.ox.ac.uk/~tvg/publications/2015/MOT_SSVM_CVPR15_V7_CamReady.pdf
arXiv:1605.08247v1 [cs.CV] 26 May 2016
Efficient SDP Inference for Fully-Connected CRFs Based on Low-Rank Decomposition
Tracklet Association with Online Target-Specific Metric Learning