AMT-Net: Adversarial Motion Transfer Network With Disentangled Shape and Pose for Realistic Image Animation

AMT-Net: Adversarial Motion Transfer Network With Disentangled Shape and Pose for Realistic Image Animation

Computer vision advancements allow motion transfer for animating static objects in images. However, current methods rely on manually collected motion labels and struggle with accurate shape and pose representation, particularly for human bodies, due to occlusions and background variations. Thus, we...

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Bibliographic Details
Main Authors: Nega Asebe Teka, Kumie Gedamu Alemu, Maregu Assefa, Feidu Akmel, Zhenting Zhou, Weijie Wu, Jianwen Chen
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Image animation
motion transfer
representation learning
Online Access:https://ieeexplore.ieee.org/document/11007652/
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