THESIS
2022
1 online resource (xv, 55 pages) : illustrations (some color)
Abstract
Conventional 3D human animation requires expertise to create an animation from scratch.
The intermediate steps such as character modeling, texture design, rigging, and skinning
could be time-consuming and a barrier for those without background of computer graphic
design. Recent research on machine learning-based realistic 3D Human reconstruction
has greatly reduced the work for 3D human modeling. However, it is still far from an
ideal one-step solution for realistic human animation from video. A painless solution
for amateurs would be direct 3D human animation generation from the video. However,
3D model reconstruction from 2D images as an ill-pose problem usually leads to unstable
performance for similar frames. Besides, research works and datasets for direct 3D human
animation from vi...[
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Conventional 3D human animation requires expertise to create an animation from scratch.
The intermediate steps such as character modeling, texture design, rigging, and skinning
could be time-consuming and a barrier for those without background of computer graphic
design. Recent research on machine learning-based realistic 3D Human reconstruction
has greatly reduced the work for 3D human modeling. However, it is still far from an
ideal one-step solution for realistic human animation from video. A painless solution
for amateurs would be direct 3D human animation generation from the video. However,
3D model reconstruction from 2D images as an ill-pose problem usually leads to unstable
performance for similar frames. Besides, research works and datasets for direct 3D human
animation from video are rarely published[1–3]. Given that there are more published
works of image-based 3D human digitization[4–6], it would be a huge advantage if one
could turn those well-trained image-based 3D human digitization neural networks to a 3D
human animation reconstruction counterpart. In view of this, we proposed a method with
the idea referenced from Deep Video Prior [7] in video restoration tasks. By exploiting
the knowledge in a well-trained single 3D human reconstruction neural network, we can
turn it into a model for 3D human animation generation of a video. Since our method
uses the original network’s prediction as a regulation prior, no extract dataset is required,
nor any handicraft networks and loss functions. The method has been tested on networks
with single and multiple intermediate training stages. We attained satisfying results on
our testing papers.
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