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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,Neural Scene Decoration from?a?Single Photograph,d a 3D model of the space, decorate, and then perform rendering. Although the task is important, it is tedious and requires tremendous effort. In this paper, we introduce a new problem of domain-specific indoor scene image synthesis, namely neural scene decoration. Given a photograph of an empty ind

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Projection

Bravura

,ChunkyGAN: Real Image Inversion via?Segments,al latent representation of the input image, our approach subdivides the input image into a set of smaller components (chunks) specified either manually or automatically using a pre-trained segmentation network. For each chunk, the latent code of a generative network is estimated locally with greate

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GAN Cocktail: Mixing GANs Without Dataset Access,ed for model merging: combining two or more pretrained generative models into a single unified one. In this work we tackle the problem of model merging, given two constraints that often come up in the real world: (1) no access to the original training data, and (2) without increasing the network siz

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,Geometry-Guided Progressive NeRF for?Generalizable and?Efficient Neural Human Rendering,mera views. Though existing NeRF-based methods can synthesize rather realistic details for human body, they tend to produce poor results when the input has self-occlusion, especially for unseen humans under sparse views. Moreover, these methods often require a large number of sampling points for ren

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Controllable Shadow Generation Using Pixel Height Maps,ot always available. Deep learning-based shadow synthesis methods learn a mapping from the light information to an object’s shadow without explicitly modeling the shadow geometry. Still, they lack control and are prone to visual artifacts. We introduce “Pixel Height", a novel geometry representation

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Orthodontics

Subspace Diffusion Generative Models,sary to run this entire process at high dimensionality and incur all the inconveniences thereof. Instead, we restrict the diffusion via projections onto . as the data distribution evolves toward noise. When applied to state-of-the-art models, our framework simultaneously . sample quality—reaching an

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,DuelGAN: A Duel Between Two Discriminators Stabilizes the?GAN Training, mode collapse. Built upon the Vanilla GAN’s two-player game between the discriminator . and the generator ., we introduce a peer discriminator . to the min-max game. Similar to previous work using two discriminators, the first role of both ., . is to distinguish between generated samples and real o