Reproduce “Deep Learning-based Image and Video Inpainting: A Survey” on OpenResearch
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About this paper
Weize Quan, Jiaxi Chen, Yanli Liu, Dong-Ming Yan, Peter Wonka
A comprehensive survey systematically categorizes and analyzes deep learning-based methods for both image and video inpainting, reviewing diverse architectures from CNNs to diffusion models. The work provides a holistic overview of technical advancements, empirical comparisons, and future challenges in filling missing visual content.
Key insights
- Deep learning models, particularly those incorporating mask-aware convolutions and attention mechanisms, enable semantic understanding and realistic content generation for complex missing regions.
- The choice of loss function presents a trade-off between pixel-level accuracy (e.g., L1/L2) and perceptual realism (e.g., adversarial, perceptual losses).
- Achieving temporal consistency is the paramount challenge and a key focus for video inpainting, addressed by leveraging inter-frame relationships.
Summary by alphaXiv.
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