Barbershop: GAN-based Image Compositing using Segmentation Masks

• URL: http://arxiv.org/abs/2106.01505v1
• Date: Wed, 2 Jun 2021 23:20:43 GMT
• Title: Barbershop: GAN-based Image Compositing using Segmentation Masks
• Authors: Peihao Zhu, Rameen Abdal, John Femiani, Peter Wonka
• Abstract summary: We present a novel solution to image blending, particularly for the problem of hairstyle transfer, based on GAN-inversion. Our results demonstrate a significant improvement over the current state of the art in a user study, with users preferring our blending solution over 95 percent of the time.
• Score: 40.85660781133709
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Seamlessly blending features from multiple images is extremely challenging because of complex relationships in lighting, geometry, and partial occlusion which cause coupling between different parts of the image. Even though recent work on GANs enables synthesis of realistic hair or faces, it remains difficult to combine them into a single, coherent, and plausible image rather than a disjointed set of image patches. We present a novel solution to image blending, particularly for the problem of hairstyle transfer, based on GAN-inversion. We propose a novel latent space for image blending which is better at preserving detail and encoding spatial information, and propose a new GAN-embedding algorithm which is able to slightly modify images to conform to a common segmentation mask. Our novel representation enables the transfer of the visual properties from multiple reference images including specific details such as moles and wrinkles, and because we do image blending in a latent-space we are able to synthesize images that are coherent. Our approach avoids blending artifacts present in other approaches and finds a globally consistent image. Our results demonstrate a significant improvement over the current state of the art in a user study, with users preferring our blending solution over 95 percent of the time.

Related papers

• Realistic and Efficient Face Swapping: A Unified Approach with Diffusion Models [69.50286698375386]
  We propose a novel approach that better harnesses diffusion models for face-swapping. We introduce a mask shuffling technique during inpainting training, which allows us to create a so-called universal model for swapping. Ours is a relatively unified approach and so it is resilient to errors in other off-the-shelf models.
  arXiv  Detail & Related papers  (2024-09-11T13:43:53Z)
• FreeCompose: Generic Zero-Shot Image Composition with Diffusion Prior [50.0535198082903]
  We offer a novel approach to image composition, which integrates multiple input images into a single, coherent image. We showcase the potential of utilizing the powerful generative prior inherent in large-scale pre-trained diffusion models to accomplish generic image composition.
  arXiv  Detail & Related papers  (2024-07-06T03:35:43Z)
• MULAN: A Multi Layer Annotated Dataset for Controllable Text-to-Image Generation [54.64194935409982]
  We introduce MuLAn: a novel dataset comprising over 44K MUlti-Layer-wise RGBA decompositions. MuLAn is the first photorealistic resource providing instance decomposition and spatial information for high quality images. We aim to encourage the development of novel generation and editing technology, in particular layer-wise solutions.
  arXiv  Detail & Related papers  (2024-04-03T14:58:00Z)
• Diverse Inpainting and Editing with GAN Inversion [4.234367850767171]
  Recent inversion methods have shown that real images can be inverted into StyleGAN's latent space. In this paper, we tackle an even more difficult task, inverting erased images into GAN's latent space for realistic inpaintings and editings.
  arXiv  Detail & Related papers  (2023-07-27T17:41:36Z)
• Image Blending Algorithm with Automatic Mask Generation [9.785996682757753]
  We propose a new image blending method with automatic mask generation. It combines semantic object detection and segmentation with mask generation to achieve deep blended images. Results on publicly available datasets show that our method outperforms other classical image blending algorithms.
  arXiv  Detail & Related papers  (2023-06-08T17:31:24Z)
• Ensembling with Deep Generative Views [72.70801582346344]
  generative models can synthesize "views" of artificial images that mimic real-world variations, such as changes in color or pose. Here, we investigate whether such views can be applied to real images to benefit downstream analysis tasks such as image classification. We use StyleGAN2 as the source of generative augmentations and investigate this setup on classification tasks involving facial attributes, cat faces, and cars.
  arXiv  Detail & Related papers  (2021-04-29T17:58:35Z)
• Bridging the Visual Gap: Wide-Range Image Blending [16.464837892640812]
  We introduce an effective deep-learning model to realize wide-range image blending. We experimentally demonstrate that our proposed method is able to produce visually appealing results.
  arXiv  Detail & Related papers  (2021-03-28T15:07:45Z)
• Image Fine-grained Inpainting [89.17316318927621]
  We present a one-stage model that utilizes dense combinations of dilated convolutions to obtain larger and more effective receptive fields. To better train this efficient generator, except for frequently-used VGG feature matching loss, we design a novel self-guided regression loss. We also employ a discriminator with local and global branches to ensure local-global contents consistency.
  arXiv  Detail & Related papers  (2020-02-07T03:45:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.