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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S2-Flow: Joint Semantic and Style Editing of Facial Images
TL;DR: In this paper , an encoder-decoder based network architecture is proposed to disentangle a GAN latent space into semantic and style spaces, enabling controlled style edits for face images independently within the same framework.
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Non-Deterministic Face Mask Removal Based on 3d Priors
TL;DR: Zhang et al. as mentioned in this paper proposed a multi-task 3D face reconstruction module with a face inpainting module, which predicts a 3DMM-based reconstructed face together with a binary occlusion map, providing dense geometrical and textural priors.
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Using Semantic Information for Defining and Detecting OOD Inputs
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TL;DR: In this article , the authors propose to use semantic information extracted from the training data of MNIST and COCO datasets and show that it not only reduces false alarms but also significantly improves the detection of OOD inputs with spurious features from training data.
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Edge Aware Domain Transformation for Face Sketch Synthesis
TL;DR: Zhang et al. as mentioned in this paper proposed a cross-domain face sketch synthesis framework based on edge-preserving filters to make the boundaries of different semantics in semantic layouts have a smooth transition.
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