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pro vyhledávání: '"Forsyth, D."'
Autor:
Sarkar, Ayush, Mai, Hanlin, Mahapatra, Amitabh, Lazebnik, Svetlana, Forsyth, D. A., Bhattad, Anand
Generative models can produce impressively realistic images. This paper demonstrates that generated images have geometric features different from those of real images. We build a set of collections of generated images, prequalified to fool simple, si
Externí odkaz:
http://arxiv.org/abs/2311.17138
Intrinsic images, in the original sense, are image-like maps of scene properties like depth, normal, albedo or shading. This paper demonstrates that StyleGAN can easily be induced to produce intrinsic images. The procedure is straightforward. We show
Externí odkaz:
http://arxiv.org/abs/2306.00987
StyleGAN's disentangled style representation enables powerful image editing by manipulating the latent variables, but accurately mapping real-world images to their latent variables (GAN inversion) remains a challenge. Existing GAN inversion methods s
Externí odkaz:
http://arxiv.org/abs/2304.14403
Autor:
Bhattad, Anand, Forsyth, D. A.
We propose a novel method, StyLitGAN, for relighting and resurfacing generated images in the absence of labeled data. Our approach generates images with realistic lighting effects, including cast shadows, soft shadows, inter-reflections, and glossy e
Externí odkaz:
http://arxiv.org/abs/2205.10351
We show how to relight a scene, depicted in a single image, such that (a) the overall shading has changed and (b) the resulting image looks like a natural image of that scene. Applications for such a procedure include generating training data and bui
Externí odkaz:
http://arxiv.org/abs/2112.04497
Autor:
Forsyth, D. A., Rock, Jason J.
Intrinsic image decomposition is the classical task of mapping image to albedo. The WHDR dataset allows methods to be evaluated by comparing predictions to human judgements ("lighter", "same as", "darker"). The best modern intrinsic image methods lea
Externí odkaz:
http://arxiv.org/abs/2011.10512
Machine learning models, especially deep neural networks (DNNs), have been shown to be vulnerable against adversarial examples which are carefully crafted samples with a small magnitude of the perturbation. Such adversarial perturbations are usually
Externí odkaz:
http://arxiv.org/abs/1904.06347
Image captioning is an ambiguous problem, with many suitable captions for an image. To address ambiguity, beam search is the de facto method for sampling multiple captions. However, beam search is computationally expensive and known to produce generi
Externí odkaz:
http://arxiv.org/abs/1805.12589
Style transfer methods produce a transferred image which is a rendering of a content image in the manner of a style image. There is a rich literature of variant methods. However, evaluation procedures are qualitative, mostly involving user studies. W
Externí odkaz:
http://arxiv.org/abs/1804.00118
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