29,993 research outputs found
Attribute-Guided Face Generation Using Conditional CycleGAN
We are interested in attribute-guided face generation: given a low-res face
input image, an attribute vector that can be extracted from a high-res image
(attribute image), our new method generates a high-res face image for the
low-res input that satisfies the given attributes. To address this problem, we
condition the CycleGAN and propose conditional CycleGAN, which is designed to
1) handle unpaired training data because the training low/high-res and high-res
attribute images may not necessarily align with each other, and to 2) allow
easy control of the appearance of the generated face via the input attributes.
We demonstrate impressive results on the attribute-guided conditional CycleGAN,
which can synthesize realistic face images with appearance easily controlled by
user-supplied attributes (e.g., gender, makeup, hair color, eyeglasses). Using
the attribute image as identity to produce the corresponding conditional vector
and by incorporating a face verification network, the attribute-guided network
becomes the identity-guided conditional CycleGAN which produces impressive and
interesting results on identity transfer. We demonstrate three applications on
identity-guided conditional CycleGAN: identity-preserving face superresolution,
face swapping, and frontal face generation, which consistently show the
advantage of our new method.Comment: ECCV 201
LiveSketch: Query Perturbations for Guided Sketch-based Visual Search
LiveSketch is a novel algorithm for searching large image collections using
hand-sketched queries. LiveSketch tackles the inherent ambiguity of sketch
search by creating visual suggestions that augment the query as it is drawn,
making query specification an iterative rather than one-shot process that helps
disambiguate users' search intent. Our technical contributions are: a triplet
convnet architecture that incorporates an RNN based variational autoencoder to
search for images using vector (stroke-based) queries; real-time clustering to
identify likely search intents (and so, targets within the search embedding);
and the use of backpropagation from those targets to perturb the input stroke
sequence, so suggesting alterations to the query in order to guide the search.
We show improvements in accuracy and time-to-task over contemporary baselines
using a 67M image corpus.Comment: Accepted to CVPR 201
Learning to Hallucinate Face Images via Component Generation and Enhancement
We propose a two-stage method for face hallucination. First, we generate
facial components of the input image using CNNs. These components represent the
basic facial structures. Second, we synthesize fine-grained facial structures
from high resolution training images. The details of these structures are
transferred into facial components for enhancement. Therefore, we generate
facial components to approximate ground truth global appearance in the first
stage and enhance them through recovering details in the second stage. The
experiments demonstrate that our method performs favorably against
state-of-the-art methodsComment: IJCAI 2017. Project page:
http://www.cs.cityu.edu.hk/~yibisong/ijcai17_sr/index.htm
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