DyStyle: Dynamic Neural Network for Multi-Attribute-Conditioned Style Editing

• URL: http://arxiv.org/abs/2109.10737v1
• Date: Wed, 22 Sep 2021 13:50:51 GMT
• Title: DyStyle: Dynamic Neural Network for Multi-Attribute-Conditioned Style Editing
• Authors: Bingchuan Li, Shaofei Cai, Wei Liu, Peng Zhang, Miao Hua, Qian He, Zili Yi
• Abstract summary: A Dynamic Style Manipulation Network (DyStyle) is proposed to perform attribute-conditioned style editing. A novel easy-to-hard training procedure is introduced for efficient and stable training of the DyStyle network. Our approach demonstrates fine-grained disentangled edits along multiple numeric and binary attributes.
• Score: 12.80013698957431
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Great diversity and photorealism have been achieved by unconditional GAN frameworks such as StyleGAN and its variations. In the meantime, persistent efforts have been made to enhance the semantic controllability of StyleGANs. For example, a dozen of style manipulation methods have been recently proposed to perform attribute-conditioned style editing. Although some of these methods work well in manipulating the style codes along one attribute, the control accuracy when jointly manipulating multiple attributes tends to be problematic. To address these limitations, we propose a Dynamic Style Manipulation Network (DyStyle) whose structure and parameters vary by input samples, to perform nonlinear and adaptive manipulation of latent codes for flexible and precise attribute control. Additionally, a novel easy-to-hard training procedure is introduced for efficient and stable training of the DyStyle network. Extensive experiments have been conducted on faces and other objects. As a result, our approach demonstrates fine-grained disentangled edits along multiple numeric and binary attributes. Qualitative and quantitative comparisons with existing style manipulation methods verify the superiority of our method in terms of the attribute control accuracy and identity preservation without compromising the photorealism. The advantage of our method is even more significant for joint multi-attribute control. The source codes are made publicly available at \href{https://github.com/phycvgan/DyStyle}{phycvgan/DyStyle}.

Related papers

• Continuous, Subject-Specific Attribute Control in T2I Models by Identifying Semantic Directions [21.371773126590874]
  We show that there exist directions in the commonly used token-level CLIP text embeddings that enable fine-grained subject-specific control of high-level attributes in text-to-image models. We introduce one efficient optimization-free and one robust optimization-based method to identify these directions for specific attributes from contrastive text prompts.
  arXiv  Detail & Related papers  (2024-03-25T18:00:42Z)
• 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)
• Style Interleaved Learning for Generalizable Person Re-identification [69.03539634477637]
  We propose a novel style interleaved learning (IL) framework for DG ReID training. Unlike conventional learning strategies, IL incorporates two forward propagations and one backward propagation for each iteration. We show that our model consistently outperforms state-of-the-art methods on large-scale benchmarks for DG ReID.
  arXiv  Detail & Related papers  (2022-07-07T07:41:32Z)
• Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning [84.8813842101747]
  Contrastive Arbitrary Style Transfer (CAST) is a new style representation learning and style transfer method via contrastive learning. Our framework consists of three key components, i.e., a multi-layer style projector for style code encoding, a domain enhancement module for effective learning of style distribution, and a generative network for image style transfer.
  arXiv  Detail & Related papers  (2022-05-19T13:11:24Z)
• 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)
• SMILE: Semantically-guided Multi-attribute Image and Layout Editing [154.69452301122175]
  Attribute image manipulation has been a very active topic since the introduction of Generative Adversarial Networks (GANs) We present a multimodal representation that handles all attributes, be it guided by random noise or images, while only using the underlying domain information of the target domain. Our method is capable of adding, removing or changing either fine-grained or coarse attributes by using an image as a reference or by exploring the style distribution space.
  arXiv  Detail & Related papers  (2020-10-05T20:15:21Z)
• StyleFlow: Attribute-conditioned Exploration of StyleGAN-Generated Images using Conditional Continuous Normalizing Flows [40.69516201141587]
  StyleFlow is an instance of conditional continuous normalizing flows in the GAN latent space conditioned by attribute features. We evaluate our method using the face and the car latent space of StyleGAN, and demonstrate fine-grained disentangled edits along various attributes on both real photographs and StyleGAN generated images.
  arXiv  Detail & Related papers  (2020-08-06T00:10:03Z)
• Multi-Attribute Guided Painting Generation [73.75835513261951]
  Controllable painting generation plays a pivotal role in image stylization. We propose a novel framework adopting multiple attributes from the painting to control the stylized results.
  arXiv  Detail & Related papers  (2020-02-26T02:22:23Z)

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.