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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FEGAN: Flexible and Efficient Face Editing With Pre-Trained Generator
TL;DR: The results show that FEGAN can flexibly perform accurate attribute editing while guaranteeing that other areas are not changed, and both qualitative and quantitative results demonstrate its advantages over existing methods.
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Style Intervention: How to Achieve Spatial Disentanglement with Style-based Generators?
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Parsing Map Guided Multi-Scale Attention Network For Face Hallucination
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TL;DR: Li et al. as discussed by the authors proposed a Learning to Aggregate and Personalize (LAP) framework for unsupervised robust 3D face modeling, which implicitly disentangles ID-consistent and scene-specific face from unconstrained photo set.
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Semantic-Driven Face Hallucination Based on Residual Network
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