194 research outputs found
Gap Processing for Adaptive Maximal Poisson-Disk Sampling
In this paper, we study the generation of maximal Poisson-disk sets with
varying radii. First, we present a geometric analysis of gaps in such disk
sets. This analysis is the basis for maximal and adaptive sampling in Euclidean
space and on manifolds. Second, we propose efficient algorithms and data
structures to detect gaps and update gaps when disks are inserted, deleted,
moved, or have their radius changed. We build on the concepts of the regular
triangulation and the power diagram. Third, we will show how our analysis can
make a contribution to the state-of-the-art in surface remeshing.Comment: 16 pages. ACM Transactions on Graphics, 201
SEAN: Image Synthesis with Semantic Region-Adaptive Normalization
We propose semantic region-adaptive normalization (SEAN), a simple but
effective building block for Generative Adversarial Networks conditioned on
segmentation masks that describe the semantic regions in the desired output
image. Using SEAN normalization, we can build a network architecture that can
control the style of each semantic region individually, e.g., we can specify
one style reference image per region. SEAN is better suited to encode,
transfer, and synthesize style than the best previous method in terms of
reconstruction quality, variability, and visual quality. We evaluate SEAN on
multiple datasets and report better quantitative metrics (e.g. FID, PSNR) than
the current state of the art. SEAN also pushes the frontier of interactive
image editing. We can interactively edit images by changing segmentation masks
or the style for any given region. We can also interpolate styles from two
reference images per region.Comment: Accepted as a CVPR 2020 oral paper. The interactive demo is available
at https://youtu.be/0Vbj9xFgoU
Functional Diffusion
We propose a new class of generative diffusion models, called functional
diffusion. In contrast to previous work, functional diffusion works on samples
that are represented by functions with a continuous domain. Functional
diffusion can be seen as an extension of classical diffusion models to an
infinite-dimensional domain. Functional diffusion is very versatile as images,
videos, audio, 3D shapes, deformations, \etc, can be handled by the same
framework with minimal changes. In addition, functional diffusion is especially
suited for irregular data or data defined in non-standard domains. In our work,
we derive the necessary foundations for functional diffusion and propose a
first implementation based on the transformer architecture. We show generative
results on complicated signed distance functions and deformation functions
defined on 3D surfaces.Comment: For the project site, see https://1zb.github.io/functional-diffusion
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