Browsing by Author "Lischinski, Dani"

Now showing 1 - 3 of 3
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    Point Pattern Synthesis via Irregular Convolution
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Tu, Peihan; Lischinski, Dani; Huang, Hui; Bommes, David and Huang, Hui
    Point pattern synthesis is a fundamental tool with various applications in computer graphics. To synthesize a point pattern, some techniques have taken an example-based approach, where the user provides a small exemplar of the target pattern. However, it remains challenging to synthesize patterns that faithfully capture the structures in the given exemplar. In this paper, we present a new example-based point pattern synthesis method that preserves both local and non-local structures present in the exemplar. Our method leverages recent neural texture synthesis techniques that have proven effective in synthesizing structured textures. The network that we present is end-to-end. It utilizes an irregular convolution layer, which converts a point pattern into a gridded feature map, to directly optimize point coordinates. The synthesis is then performed by matching inter- and intra-correlations of the responses produced by subsequent convolution layers. We demonstrate that our point pattern synthesis qualitatively outperforms state-of-the-art methods on challenging structured patterns, and enables various graphical applications, such as object placement in natural scenes, creative element patterns or realistic urban layouts in a 3D virtual environment.
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    What's in a Decade? Transforming Faces Through Time
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Chen, Eric Ming; Sun, Jin; Khandelwal, Apoorv; Lischinski, Dani; Snavely, Noah; Averbuch-Elor, Hadar; Myszkowski, Karol; Niessner, Matthias
    How can one visually characterize photographs of people over time? In this work, we describe the Faces Through Time dataset, which contains over a thousand portrait images per decade from the 1880s to the present day. Using our new dataset, we devise a framework for resynthesizing portrait images across time, imagining how a portrait taken during a particular decade might have looked like had it been taken in other decades. Our framework optimizes a family of per-decade generators that reveal subtle changes that differentiate decades-such as different hairstyles or makeup-while maintaining the identity of the input portrait. Experiments show that our method can more effectively resynthesizing portraits across time compared to state-of-theart image-to-image translation methods, as well as attribute-based and language-guided portrait editing models. Our code and data will be available at facesthroughtime.github.io.
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    What's in a Face? Metric Learning for Face Characterization
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Sendik, Omry; Lischinski, Dani; Cohen-Or, Daniel; Alliez, Pierre and Pellacini, Fabio
    We present a method for determining which facial parts (mouth, nose, etc.) best characterize an individual, given a set of that individual's portraits. We introduce a novel distinctiveness analysis of a set of portraits, which leverages the deep features extracted by a pre-trained face recognition CNN and a hair segmentation FCN, in the context of a weakly supervised metric learning scheme. Our analysis enables the generation of a polarized class activation map (PCAM) for an individual's portrait via a transformation that localizes and amplifies the discriminative regions of the deep feature maps extracted by the aforementioned networks. A user study that we conducted shows that there is a surprisingly good agreement between the face parts that users indicate as characteristic and the face parts automatically selected by our method. We demonstrate a few applications of our method, including determining the most and the least representative portraits among a set of portraits of an individual, and the creation of facial hybrids: portraits that combine the characteristic recognizable facial features of two individuals. Our face characterization analysis is also effective for ranking portraits in order to find an individual's look-alikes (Doppelgängers).