Titlebook: Computer Vision – ECCV 2022; 17th European Confer Shai Avidan,Gabriel Brostow,Tal Hassner Conference proceedings 2022 The Editor(s) (if app

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Controllable Shadow Generation Using Pixel Height Maps,tor to apply softness to a hard shadow based on a softness input parameter. Qualitative and quantitative evaluations demonstrate that the proposed Pixel Height significantly improves the quality of the shadow generation while allowing for controllability.

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,DuelGAN: A Duel Between Two Discriminators Stabilizes the GAN Training,x game formed among .. We offer convergence behavior of DuelGAN as well as stability of the min-max game. It’s worth mentioning that DuelGAN operates in the unsupervised setting, and the duel between . and . does not need any label supervision. Experiments results on a synthetic dataset and on real-

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Per-Olov Johansson,Bengt Kriström FID of 2.17 on unconditional CIFAR-10—and . the computational cost of inference for the same number of denoising steps. Our framework is fully compatible with continuous-time diffusion and retains its flexible capabilities, including exact log-likelihoods and controllable generation. Code is available at ..

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0302-9743 ruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation..978-3-031-20049-6978-3-031-20050-2Series ISSN 0302-9743 Series E-ISSN 1611-3349