Multi-Directional Subspace Editing in Style-Space

• URL: http://arxiv.org/abs/2211.11825v3
• Date: Wed, 23 Aug 2023 18:52:24 GMT
• Title: Multi-Directional Subspace Editing in Style-Space
• Authors: Chen Naveh and Yacov Hel-Or
• Abstract summary: This paper describes a new technique for finding disentangled semantic directions in the latent space of StyleGAN. Our model is capable of editing a single attribute in multiple directions, resulting in a range of possible generated images.
• Score: 6.282068591820945
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper describes a new technique for finding disentangled semantic directions in the latent space of StyleGAN. Our method identifies meaningful orthogonal subspaces that allow editing of one human face attribute, while minimizing undesired changes in other attributes. Our model is capable of editing a single attribute in multiple directions, resulting in a range of possible generated images. We compare our scheme with three state-of-the-art models and show that our method outperforms them in terms of face editing and disentanglement capabilities. Additionally, we suggest quantitative measures for evaluating attribute separation and disentanglement, and exhibit the superiority of our model with respect to those measures.

Related papers

• FUSE-ing Language Models: Zero-Shot Adapter Discovery for Prompt Optimization Across Tokenizers [55.2480439325792]
  We propose FUSE, an approach to approximating an adapter layer that maps from one model's textual embedding space to another, even across different tokenizers. We show the efficacy of our approach via multi-objective optimization over vision-language and causal language models for image captioning and sentiment-based image captioning.
  arXiv  Detail & Related papers  (2024-08-09T02:16:37Z)
• Exploring Attribute Variations in Style-based GANs using Diffusion Models [48.98081892627042]
  We formulate the task of textitdiverse attribute editing by modeling the multidimensional nature of attribute edits. We capitalize on disentangled latent spaces of pretrained GANs and train a Denoising Diffusion Probabilistic Model (DDPM) to learn the latent distribution for diverse edits.
  arXiv  Detail & Related papers  (2023-11-27T18:14:03Z)
• ManiCLIP: Multi-Attribute Face Manipulation from Text [104.30600573306991]
  We present a novel multi-attribute face manipulation method based on textual descriptions. Our method generates natural manipulated faces with minimal text-irrelevant attribute editing.
  arXiv  Detail & Related papers  (2022-10-02T07:22:55Z)
• Everything is There in Latent Space: Attribute Editing and Attribute Style Manipulation by StyleGAN Latent Space Exploration [39.18239951479647]
  We present Few-shot Latent-based Attribute Manipulation and Editing (FLAME) FLAME is a framework to perform highly controlled image editing by latent space manipulation. We generate diverse attribute styles in disentangled manner.
  arXiv  Detail & Related papers  (2022-07-20T12:40:32Z)
• Attribute-specific Control Units in StyleGAN for Fine-grained Image Manipulation [57.99007520795998]
  We discover attribute-specific control units, which consist of multiple channels of feature maps and modulation styles. Specifically, we collaboratively manipulate the modulation style channels and feature maps in control units to obtain the semantic and spatial disentangled controls. We move the modulation style along a specific sparse direction vector and replace the filter-wise styles used to compute the feature maps to manipulate these control units.
  arXiv  Detail & Related papers  (2021-11-25T10:42:10Z)
• Disentangled Face Attribute Editing via Instance-Aware Latent Space Search [30.17338705964925]
  A rich set of semantic directions exist in the latent space of Generative Adversarial Networks (GANs) Existing methods may suffer poor attribute variation disentanglement, leading to unwanted change of other attributes when altering the desired one. We propose a novel framework (IALS) that performs Instance-Aware Latent-Space Search to find semantic directions for disentangled attribute editing.
  arXiv  Detail & Related papers  (2021-05-26T16:19:08Z)
• Unsupervised Discovery of Disentangled Manifolds in GANs [74.24771216154105]
  Interpretable generation process is beneficial to various image editing applications. We propose a framework to discover interpretable directions in the latent space given arbitrary pre-trained generative adversarial networks.
  arXiv  Detail & Related papers  (2020-11-24T02:18:08Z)
• Learning to Manipulate Individual Objects in an Image [71.55005356240761]
  We describe a method to train a generative model with latent factors that are independent and localized. This means that perturbing the latent variables affects only local regions of the synthesized image, corresponding to objects. Unlike other unsupervised generative models, ours enables object-centric manipulation, without requiring object-level annotations.
  arXiv  Detail & Related papers  (2020-04-11T21:50:20Z)

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.