“Revitalizing Traditional Animation: Pre-Composite Frame Interpolation as a Production Catalyst” by Brown and Bourgeois – ACM SIGGRAPH HISTORY ARCHIVES

“Revitalizing Traditional Animation: Pre-Composite Frame Interpolation as a Production Catalyst” by Brown and Bourgeois

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Title:

    Revitalizing Traditional Animation: Pre-Composite Frame Interpolation as a Production Catalyst

Session/Category Title:   Animation & Simulation


Presenter(s)/Author(s):



Abstract:


    For over a century, audiences of all ages have been entertained by the art of animation. However, the industry has shifted towards computer-generated animation, leading to a decline in traditional, hand-drawn animation. There is a unique opportunity for computational innovation with a novel approach to revitalizing traditional animation by focusing on its creative methods and production. Before implementing current computational techniques, we must start putting ourselves into the minds of imaginative animators to realize these techniques. Only then can we develop new tools designed to empower these artists, not ones designed to supplant them.

References:


    [1]
    Wenbo Bao, Wei-Sheng Lai, Xiaoyun Zhang, Zhiyong Gao, and Ming-Hsuan Yang. 2021. MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement. IEEE Transactions on Pattern Analysis and Machine Intelligence 43, 3 (2021), 933?948. https://doi.org/10.1109/TPAMI.2019.2941941

    [2]
    Shuhong Chen and Matthias Zwicker. 2022. Improving the perceptual quality of 2d animation interpolation. In European Conference on Computer Vision. Springer, Switzerland, 271?287.

    [3]
    Nathan Dupouy. 2020. Young Ganondorf. https://cdna.artstation.com/p/assets/images/images/033/537/444/original/nathan-dupouy-ganonruncyclesd.gif?1609882820

    [4]
    Xiaoyu Li, Bo Zhang, Jing Liao, and Pedro V. Sander. 2022. Deep Sketch-Guided Cartoon Video Inbetweening. IEEE Transactions on Visualization and Computer Graphics 28, 8 (2022), 2938?2952. https://doi.org/10.1109/TVCG.2021.3049419

    [5]
    Simon Niklaus, Long Mai, and Feng Liu. 2017. Video Frame Interpolation via Adaptive Separable Convolution. https://doi.org/10.48550/ARXIV.1708.01692 arxiv:1708.01692 [cs.CV]

    [6]
    Yuliya Pauliukevich. 2022. Summer City Skyline Urban View Fence. City Vectors by Vecteezy

    [7]
    Wang Shen, Cheng Ming, Wenbo Bao, Guangtao Zhai, Li Chen, and Zhiyong Gao. 2022. Enhanced Deep Animation Video Interpolation. arxiv:2206.12657 [cs.CV]

    [8]
    Li Siyao, Shiyu Zhao, Weijiang Yu, Wenxiu Sun, Dimitris Metaxas, Chen Change Loy, and Ziwei Liu. 2021. Deep Animation Video Interpolation in the Wild. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 6583?6591. https://doi.org/10.1109/CVPR46437.2021.00652

    [9]
    Ryohei Takeshita and Tatsuya Takahashi. 2017. Excerpt from “Eromanga Sensei”. https://imgur.com/sagiri-caramelldansen-b6fPgNi


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