Results 41 to 50 of about 131,036 (273)
Controlling Perceptual Factors in Neural Style Transfer
, 2017 Neural Style Transfer has shown very exciting results enabling new forms of image manipulation. Here we extend the existing method to introduce control over spatial location, colour information and across spatial scale.
Bethge, Matthias +4 more
core +1 more source
Fast Neural Style Transfer for Motion Data [PDF]
IEEE Computer Graphics and Applications, 2017 Automating motion style transfer can help save animators time by allowing them to produce a single set of motions, which can then be automatically adapted for use with different characters. The proposed fast, efficient technique for performing neural style transfer of human motion data uses a feed-forward neural network trained on a large motion ...
Komura, Taku +3 more
openaire +4 more sources
MIDI-VAE: Modeling Dynamics and Instrumentation of Music with Applications to Style Transfer
, 2018 We introduce MIDI-VAE, a neural network model based on Variational Autoencoders that is capable of handling polyphonic music with multiple instrument tracks, as well as modeling the dynamics of music by incorporating note durations and velocities.
Brunner, Gino +3 more
core +1 more source
Fast Face-swap Using Convolutional Neural Networks [PDF]
, 2017 We consider the problem of face swapping in images, where an input identity is transformed into a target identity while preserving pose, facial expression, and lighting. To perform this mapping, we use convolutional neural networks trained to capture the
Dambre, Joni +3 more
core +3 more sources
Proceedings of the Symposium on Non-Photorealistic Animation and Rendering, 2017 In this meta paper we discuss image-based artistic rendering (IB-AR) based on neural style transfer (NST) and argue, while NST may represent a paradigm shift for IB-AR, that it also has to evolve as an interactive tool that considers the design aspects and mechanisms of artwork production.
Semmo, Amir +2 more
openaire +1 more source
SAMStyler: Enhancing Visual Creativity With Neural Style Transfer and Segment Anything Model (SAM)
IEEE Access, 2023 Neural Style Transfer (NST) is a popular technique of computer vision where the content of an image is blended with the style of another, which results in a fused image with certain properties of both original images.
Konstantinos Psychogyios +5 more
doaj +1 more source
Bringing Impressionism to Life with Neural Style Transfer in Come Swim
, 2017 Neural Style Transfer is a striking, recently-developed technique that uses neural networks to artistically redraw an image in the style of a source style image. This paper explores the use of this technique in a production setting, applying Neural Style
Joshi, Bhautik +2 more
core +1 more source
Neural saliency algorithm guide bi-directional visual perception style transfer
CAAI Transactions on Intelligence Technology, 2019 The artistic style transfer of images aims to synthesise novel images by combining the content of one image with the style of another, which is a long-standing research topic and already has been widely applied in real world.
Chunbiao Zhu +6 more
doaj +1 more source
Sand Painting Generation Based on Convolutional Neural Networks
Journal of ImagingNeural style transfer is an algorithm that transfers the style of one image to another image and converts the style of the second image while preserving its content.
Chin-Chen Chang, Ping-Hao Peng
doaj +1 more source
Image Neural Style Transfer With Preserving the Salient Regions
IEEE Access, 2019 Neural style transfer recently has become one of the most popular topics in academic research and industrial application. The existing methods can generate synthetic images by transferring different styles of some images to another given content images ...
Yijun Liu +6 more
doaj +1 more source