Structurally consistent, diverse, colorization using generative modeling and GTAV
Jagarlamudi, Shreya
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https://hdl.handle.net/2142/105407
Description
Title
Structurally consistent, diverse, colorization using generative modeling and GTAV
Author(s)
Jagarlamudi, Shreya
Contributor(s)
Schwing, Alexander G.
Issue Date
2018-12
Keyword(s)
Video Colorization
Gaussian Conditional Random Field
VAE
MDN
Abstract
Colorizing videos is an important task in the media industry and in object tracking due to the
nature of temporal coherency that comes with color. Doing this automatically, consistently and
diversely has been a challenging problem in computer vision. Existing work in this field only deals
with part of the problem to achieve temporally incoherent results by stitching colorized images
together. In this thesis, we use a conditional random field based, variational auto encoder to
model structural consistency and diversity of videos and train it on our custom dataset generated
from GTA V. Instead of predicting colors for each pixel individually, we learn both spatial
correlations between pixels in a frame and temporal correlations between pixels across different
frames of each video in order to keep colorization consistent across the video. The model also
includes a mixture density network to create a distribution of diverse colorizations per gray-scale
video input. We also allow optional user controllability for more realistic colorizations. We
demonstrate that our model achieves spatially and temporally consistent, diverse colorizations
of gray-level videos.
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