ConsistentID: Portrait Generation with Multimodal Fine-Grained Identity Preserving

• URL: http://arxiv.org/abs/2404.16771v1
• Date: Thu, 25 Apr 2024 17:23:43 GMT
• Title: ConsistentID: Portrait Generation with Multimodal Fine-Grained Identity Preserving
• Authors: Jiehui Huang, Xiao Dong, Wenhui Song, Hanhui Li, Jun Zhou, Yuhao Cheng, Shutao Liao, Long Chen, Yiqiang Yan, Shengcai Liao, Xiaodan Liang,
• Abstract summary: ConsistentID is an innovative method crafted for diverseidentity-preserving portrait generation under fine-grained multimodal facial prompts. We present a fine-grained portrait dataset, FGID, with over 500,000 facial images, offering greater diversity and comprehensiveness than existing public facial datasets.
• Score: 66.09976326184066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion-based technologies have made significant strides, particularly in personalized and customized facialgeneration. However, existing methods face challenges in achieving high-fidelity and detailed identity (ID)consistency, primarily due to insufficient fine-grained control over facial areas and the lack of a comprehensive strategy for ID preservation by fully considering intricate facial details and the overall face. To address these limitations, we introduce ConsistentID, an innovative method crafted for diverseidentity-preserving portrait generation under fine-grained multimodal facial prompts, utilizing only a single reference image. ConsistentID comprises two key components: a multimodal facial prompt generator that combines facial features, corresponding facial descriptions and the overall facial context to enhance precision in facial details, and an ID-preservation network optimized through the facial attention localization strategy, aimed at preserving ID consistency in facial regions. Together, these components significantly enhance the accuracy of ID preservation by introducing fine-grained multimodal ID information from facial regions. To facilitate training of ConsistentID, we present a fine-grained portrait dataset, FGID, with over 500,000 facial images, offering greater diversity and comprehensiveness than existing public facial datasets. % such as LAION-Face, CelebA, FFHQ, and SFHQ. Experimental results substantiate that our ConsistentID achieves exceptional precision and diversity in personalized facial generation, surpassing existing methods in the MyStyle dataset. Furthermore, while ConsistentID introduces more multimodal ID information, it maintains a fast inference speed during generation.

Related papers

• ID$^3$: Identity-Preserving-yet-Diversified Diffusion Models for Synthetic Face Recognition [60.15830516741776]
  Synthetic face recognition (SFR) aims to generate datasets that mimic the distribution of real face data. We introduce a diffusion-fueled SFR model termed $textID3$. $textID3$ employs an ID-preserving loss to generate diverse yet identity-consistent facial appearances.
  arXiv  Detail & Related papers  (2024-09-26T06:46:40Z)
• InstantFamily: Masked Attention for Zero-shot Multi-ID Image Generation [0.0]
  "InstantFamily" is an approach that employs a novel cross-attention mechanism and a multimodal embedding stack to achieve zero-shot multi-ID image generation. Our method effectively preserves ID as it utilizes global and local features from a pre-trained face recognition model integrated with text conditions.
  arXiv  Detail & Related papers  (2024-04-30T10:16:21Z)
• HFORD: High-Fidelity and Occlusion-Robust De-identification for Face Privacy Protection [60.63915939982923]
  Face de-identification is a practical way to solve the identity protection problem. The existing facial de-identification methods have revealed several problems. We present a High-Fidelity and Occlusion-Robust De-identification (HFORD) method to deal with these issues.
  arXiv  Detail & Related papers  (2023-11-15T08:59:02Z)
• FaceDancer: Pose- and Occlusion-Aware High Fidelity Face Swapping [62.38898610210771]
  We present a new single-stage method for subject face swapping and identity transfer, named FaceDancer. We have two major contributions: Adaptive Feature Fusion Attention (AFFA) and Interpreted Feature Similarity Regularization (IFSR)
  arXiv  Detail & Related papers  (2022-10-19T11:31:38Z)
• A Systematical Solution for Face De-identification [6.244117712209321]
  In different tasks, people have various requirements for face de-identification (De-ID) We propose a systematical solution compatible for these De-ID operations. Our method can flexibly de-identify the face data in various ways and the processed images have high image quality.
  arXiv  Detail & Related papers  (2021-07-19T02:02:51Z)
• Semantic Consistency and Identity Mapping Multi-Component Generative Adversarial Network for Person Re-Identification [39.605062525247135]
  We propose a semantic consistency and identity mapping multi-component generative adversarial network (SC-IMGAN) which provides style adaptation from one to many domains. Our proposed method outperforms state-of-the-art techniques on six challenging person Re-ID datasets.
  arXiv  Detail & Related papers  (2021-04-28T14:12:29Z)
• IdentityDP: Differential Private Identification Protection for Face Images [17.33916392050051]
  Face de-identification, also known as face anonymization, refers to generating another image with similar appearance and the same background, while the real identity is hidden. We propose IdentityDP, a face anonymization framework that combines a data-driven deep neural network with a differential privacy mechanism. Our model can effectively obfuscate the identity-related information of faces, preserve significant visual similarity, and generate high-quality images.
  arXiv  Detail & Related papers  (2021-03-02T14:26:00Z)
• DVG-Face: Dual Variational Generation for Heterogeneous Face Recognition [85.94331736287765]
  We formulate HFR as a dual generation problem, and tackle it via a novel Dual Variational Generation (DVG-Face) framework. We integrate abundant identity information of large-scale visible data into the joint distribution. Massive new diverse paired heterogeneous images with the same identity can be generated from noises.
  arXiv  Detail & Related papers  (2020-09-20T09:48:24Z)
• Dual-Attention GAN for Large-Pose Face Frontalization [59.689836951934694]
  We present a novel Dual-Attention Generative Adversarial Network (DA-GAN) for photo-realistic face frontalization. Specifically, a self-attention-based generator is introduced to integrate local features with their long-range dependencies. A novel face-attention-based discriminator is applied to emphasize local features of face regions.
  arXiv  Detail & Related papers  (2020-02-17T20:00:56Z)

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.