Automated colorization of animated characters

This project undertakes the task of automating colorization in animations by exploring Frame-by-Frame Prediction Models and T-pose Reference-based Prediction approaches. Emphasizing the imperatives of reducing manual labor burden on Digital Painters, the study advocates for the adoption of innovativ...

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Bibliographic Details
Main Author: Lin, Jiajun
Other Authors: Chen Change Loy
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/175784
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Institution: Nanyang Technological University
Language: English
Description
Summary:This project undertakes the task of automating colorization in animations by exploring Frame-by-Frame Prediction Models and T-pose Reference-based Prediction approaches. Emphasizing the imperatives of reducing manual labor burden on Digital Painters, the study advocates for the adoption of innovative frameworks. The analysis delves into Frame-by-Frame Prediction Models, analysing performance of Segment Matching and Optical Flow through RAFT, each presenting its own merits and drawbacks. Additionally, image segmentation models, including PSANet and PSPNet, are investigated for possible integration into Segment Matching Models to achieve T-pose Reference-based Prediction. Moving forward, further research and development are crucial to enhance image segmentation methods and seamlessly integrate them into colorization workflows, ushering in automation in animation production.