1,207 research outputs found
A Temporally Coherent Neural Algorithm for Artistic Style Transfer
Within the fields of visual effects and animation, humans have historically spent countless painstaking hours mastering the skill of drawing frame-by-frame animations. One such animation technique that has been widely used in the animation and visual effects industry is called rotoscoping and has allowed uniquely stylized animations to capture the motion of real life action sequences, however it is a very complex and time consuming process. Automating this arduous technique would free animators from performing frame by frame stylization and allow them to concentrate on their own artistic contributions. This thesis introduces a new artificial system based on an existing neural style transfer method which creates artistically stylized animations that simultaneously reproduce both the motion of the original videos that they are derived from and the unique style of a given artistic work. This system utilizes a convolutional neural network framework to extract a hierarchy of image features used for generating images that appear visually similar to a given artistic style while at the same time faithfully preserving temporal content. The use of optical flow allows the combination of style and content to be integrated directly with the apparent motion over frames of a video to produce smooth and visually appealing transitions. The implementation described in this thesis demonstrates how biologically-inspired systems such as convolutional neural networks are rapidly approaching human-level behavior in tasks that were once thought impossible for computers. Such a complex task elucidates the current and future technical and artistic capabilities of such biologically-inspired neural systems as their horizons expand exponentially. Further, this research provides unique insights into the way that humans perceive and utilize temporal information in everyday tasks. A secondary implementation that is explored in this thesis seeks to improve existing convolutional neural networks using a biological approach to the way these models adapt to their inputs. This implementation shows how these pattern recognition systems can be greatly improved by integrating recent neuroscience research into already biologically inspired systems. Such a novel hybrid activation function model replicates recent findings in the field of neuroscience and shows significant advantages over existing static activation functions
Transport-Based Neural Style Transfer for Smoke Simulations
Artistically controlling fluids has always been a challenging task.
Optimization techniques rely on approximating simulation states towards target
velocity or density field configurations, which are often handcrafted by
artists to indirectly control smoke dynamics. Patch synthesis techniques
transfer image textures or simulation features to a target flow field. However,
these are either limited to adding structural patterns or augmenting coarse
flows with turbulent structures, and hence cannot capture the full spectrum of
different styles and semantically complex structures. In this paper, we propose
the first Transport-based Neural Style Transfer (TNST) algorithm for volumetric
smoke data. Our method is able to transfer features from natural images to
smoke simulations, enabling general content-aware manipulations ranging from
simple patterns to intricate motifs. The proposed algorithm is physically
inspired, since it computes the density transport from a source input smoke to
a desired target configuration. Our transport-based approach allows direct
control over the divergence of the stylization velocity field by optimizing
incompressible and irrotational potentials that transport smoke towards
stylization. Temporal consistency is ensured by transporting and aligning
subsequent stylized velocities, and 3D reconstructions are computed by
seamlessly merging stylizations from different camera viewpoints.Comment: ACM Transaction on Graphics (SIGGRAPH ASIA 2019), additional
materials: http://www.byungsoo.me/project/neural-flow-styl
Characterizing and Improving Stability in Neural Style Transfer
Recent progress in style transfer on images has focused on improving the
quality of stylized images and speed of methods. However, real-time methods are
highly unstable resulting in visible flickering when applied to videos. In this
work we characterize the instability of these methods by examining the solution
set of the style transfer objective. We show that the trace of the Gram matrix
representing style is inversely related to the stability of the method. Then,
we present a recurrent convolutional network for real-time video style transfer
which incorporates a temporal consistency loss and overcomes the instability of
prior methods. Our networks can be applied at any resolution, do not re- quire
optical flow at test time, and produce high quality, temporally consistent
stylized videos in real-time
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