7,363 research outputs found
Neural Face Editing with Intrinsic Image Disentangling
Traditional face editing methods often require a number of sophisticated and
task specific algorithms to be applied one after the other --- a process that
is tedious, fragile, and computationally intensive. In this paper, we propose
an end-to-end generative adversarial network that infers a face-specific
disentangled representation of intrinsic face properties, including shape (i.e.
normals), albedo, and lighting, and an alpha matte. We show that this network
can be trained on "in-the-wild" images by incorporating an in-network
physically-based image formation module and appropriate loss functions. Our
disentangling latent representation allows for semantically relevant edits,
where one aspect of facial appearance can be manipulated while keeping
orthogonal properties fixed, and we demonstrate its use for a number of facial
editing applications.Comment: CVPR 2017 ora
Stylizing Face Images via Multiple Exemplars
We address the problem of transferring the style of a headshot photo to face
images. Existing methods using a single exemplar lead to inaccurate results
when the exemplar does not contain sufficient stylized facial components for a
given photo. In this work, we propose an algorithm to stylize face images using
multiple exemplars containing different subjects in the same style. Patch
correspondences between an input photo and multiple exemplars are established
using a Markov Random Field (MRF), which enables accurate local energy transfer
via Laplacian stacks. As image patches from multiple exemplars are used, the
boundaries of facial components on the target image are inevitably
inconsistent. The artifacts are removed by a post-processing step using an
edge-preserving filter. Experimental results show that the proposed algorithm
consistently produces visually pleasing results.Comment: In CVIU 2017. Project Page:
http://www.cs.cityu.edu.hk/~yibisong/cviu17/index.htm
Pushing the Limits of 3D Color Printing: Error Diffusion with Translucent Materials
Accurate color reproduction is important in many applications of 3D printing,
from design prototypes to 3D color copies or portraits. Although full color is
available via other technologies, multi-jet printers have greater potential for
graphical 3D printing, in terms of reproducing complex appearance properties.
However, to date these printers cannot produce full color, and doing so poses
substantial technical challenges, from the shear amount of data to the
translucency of the available color materials. In this paper, we propose an
error diffusion halftoning approach to achieve full color with multi-jet
printers, which operates on multiple isosurfaces or layers within the object.
We propose a novel traversal algorithm for voxel surfaces, which allows the
transfer of existing error diffusion algorithms from 2D printing. The resulting
prints faithfully reproduce colors, color gradients and fine-scale details.Comment: 15 pages, 14 figures; includes supplemental figure
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