MaskGAN: Towards Diverse and Interactive Facial Image Manipulation
Cheng-Han Lee,Ziwei Liu,Lingyun Wu,Ping Luo +3 more
- pp 5549-5558
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TLDR
MaskGAN as mentioned in this paper proposes MaskGAN to enable diverse and interactive face manipulation by learning style mapping between a free-form user modified mask and a target image, enabling diverse generation results.Abstract:
Facial image manipulation has achieved great progress in recent years. However, previous methods either operate on a predefined set of face attributes or leave users little freedom to interactively manipulate images. To overcome these drawbacks, we propose a novel framework termed MaskGAN, enabling diverse and interactive face manipulation. Our key insight is that semantic masks serve as a suitable intermediate representation for flexible face manipulation with fidelity preservation. MaskGAN has two main components: 1) Dense Mapping Network (DMN) and 2) Editing Behavior Simulated Training (EBST). Specifically, DMN learns style mapping between a free-form user modified mask and a target image, enabling diverse generation results. EBST models the user editing behavior on the source mask, making the overall framework more robust to various manipulated inputs. Specifically, it introduces dual-editing consistency as the auxiliary supervision signal. To facilitate extensive studies, we construct a large-scale high-resolution face dataset with fine-grained mask annotations named CelebAMask-HQ. MaskGAN is comprehensively evaluated on two challenging tasks: attribute transfer and style copy, demonstrating superior performance over other state-of-the-art methods. The code, models, and dataset are available at https://github.com/switchablenorms/CelebAMask-HQ.read more
Citations
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Bi-level Feature Alignment for Versatile Image Translation and Manipulation
TL;DR: Zhang et al. as mentioned in this paper proposed a bi-level feature alignment strategy that adopts a top-k operation to rank block-wise features followed by dense attention between block features which reduces memory cost substantially.
Proceedings ArticleDOI
Copyright Protection and Accountability of Generative AI: Attack, Watermarking and Attribution
Haonan Zhong,Jiamin Chang,Ziyue Yang,Tingmin Wu,Pathum Chamikara Mahawaga Arachchige,Chehara Pathmabandu,Minhui Xue +6 more
TL;DR: In this article , the authors provide a comprehensive overview of the current state of the copyright protection measures for GANs, evaluate their performance across a diverse range of GAN architectures, and identify the factors that affect their performance and future research directions.
Proceedings ArticleDOI
Towards Controllable and Photorealistic Region-wise Image Manipulation
TL;DR: In this paper, a generative model with auto-encoder architecture for per-region style manipulation is presented, which applies a code consistency loss to enforce an explicit disentanglement between content and style latent representations.
Book ChapterDOI
Foreground-Guided Facial Inpainting with Fidelity Preservation
TL;DR: In this article, a foreground-guided facial inpainting framework was proposed that can extract and generate facial features using convolutional neural network layers and introduces the use of foreground segmentation masks to preserve the fidelity.
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