CA-Edit: Causality-Aware Condition Adapter for High-Fidelity Local Facial Attribute Editing
- URL: http://arxiv.org/abs/2412.13565v1
- Date: Wed, 18 Dec 2024 07:33:22 GMT
- Title: CA-Edit: Causality-Aware Condition Adapter for High-Fidelity Local Facial Attribute Editing
- Authors: Xiaole Xian, Xilin He, Zenghao Niu, Junliang Zhang, Weicheng Xie, Siyang Song, Zitong Yu, Linlin Shen,
- Abstract summary: A novel data utilization strategy is introduced to construct datasets consisting of attribute-text triples from a data-driven perspective.
A Skin Transition Frequency Guidance technique is introduced for the local modeling of contextual causality.
- Score: 41.92598830147057
- License:
- Abstract: For efficient and high-fidelity local facial attribute editing, most existing editing methods either require additional fine-tuning for different editing effects or tend to affect beyond the editing regions. Alternatively, inpainting methods can edit the target image region while preserving external areas. However, current inpainting methods still suffer from the generation misalignment with facial attributes description and the loss of facial skin details. To address these challenges, (i) a novel data utilization strategy is introduced to construct datasets consisting of attribute-text-image triples from a data-driven perspective, (ii) a Causality-Aware Condition Adapter is proposed to enhance the contextual causality modeling of specific details, which encodes the skin details from the original image while preventing conflicts between these cues and textual conditions. In addition, a Skin Transition Frequency Guidance technique is introduced for the local modeling of contextual causality via sampling guidance driven by low-frequency alignment. Extensive quantitative and qualitative experiments demonstrate the effectiveness of our method in boosting both fidelity and editability for localized attribute editing. The code is available at https://github.com/connorxian/CA-Edit.
Related papers
- Learning Feature-Preserving Portrait Editing from Generated Pairs [11.122956539965761]
We propose a training-based method leveraging auto-generated paired data to learn desired editing.
Our method achieves state-of-the-art quality, quantitatively and qualitatively.
arXiv Detail & Related papers (2024-07-29T23:19:42Z) - MAG-Edit: Localized Image Editing in Complex Scenarios via Mask-Based
Attention-Adjusted Guidance [28.212908146852197]
We develop MAG-Edit, a training-free, inference-stage optimization method, which enables localized image editing in complex scenarios.
In particular, MAG-Edit optimize the noise latent feature in diffusion models by maximizing two mask-based cross-attention constraints.
arXiv Detail & Related papers (2023-12-18T17:55:44Z) - AdapEdit: Spatio-Temporal Guided Adaptive Editing Algorithm for
Text-Based Continuity-Sensitive Image Editing [24.9487669818162]
We propose atemporal guided adaptive editing algorithm AdapEdit, which realizes adaptive image editing.
Our approach has a significant advantage in preserving model priors and does not require model training, fine-tuning extra data, or optimization.
We present our results over a wide variety of raw images and editing instructions, demonstrating competitive performance and showing it significantly outperforms the previous approaches.
arXiv Detail & Related papers (2023-12-13T09:45:58Z) - Customize your NeRF: Adaptive Source Driven 3D Scene Editing via
Local-Global Iterative Training [61.984277261016146]
We propose a CustomNeRF model that unifies a text description or a reference image as the editing prompt.
To tackle the first challenge, we propose a Local-Global Iterative Editing (LGIE) training scheme that alternates between foreground region editing and full-image editing.
For the second challenge, we also design a class-guided regularization that exploits class priors within the generation model to alleviate the inconsistency problem.
arXiv Detail & Related papers (2023-12-04T06:25:06Z) - iEdit: Localised Text-guided Image Editing with Weak Supervision [53.082196061014734]
We propose a novel learning method for text-guided image editing.
It generates images conditioned on a source image and a textual edit prompt.
It shows favourable results against its counterparts in terms of image fidelity, CLIP alignment score and qualitatively for editing both generated and real images.
arXiv Detail & Related papers (2023-05-10T07:39:14Z) - Enjoy Your Editing: Controllable GANs for Image Editing via Latent Space
Navigation [136.53288628437355]
Controllable semantic image editing enables a user to change entire image attributes with few clicks.
Current approaches often suffer from attribute edits that are entangled, global image identity changes, and diminished photo-realism.
We propose quantitative evaluation strategies for measuring controllable editing performance, unlike prior work which primarily focuses on qualitative evaluation.
arXiv Detail & Related papers (2021-02-01T21:38:36Z) - MagGAN: High-Resolution Face Attribute Editing with Mask-Guided
Generative Adversarial Network [145.4591079418917]
MagGAN learns to only edit the facial parts that are relevant to the desired attribute changes.
A novel mask-guided conditioning strategy is introduced to incorporate the influence region of each attribute change into the generator.
A multi-level patch-wise discriminator structure is proposed to scale our model for high-resolution ($1024 times 1024$) face editing.
arXiv Detail & Related papers (2020-10-03T20:56:16Z) - PA-GAN: Progressive Attention Generative Adversarial Network for Facial
Attribute Editing [67.94255549416548]
We propose a progressive attention GAN (PA-GAN) for facial attribute editing.
Our approach achieves correct attribute editing with irrelevant details much better preserved compared with the state-of-the-arts.
arXiv Detail & Related papers (2020-07-12T03:04:12Z) - Exemplar-based Generative Facial Editing [2.272764591035106]
We propose a novel generative approach for exemplar based facial editing in the form of the region inpainting.
Experimental results demonstrate our method can produce diverse and personalized face editing results.
arXiv Detail & Related papers (2020-05-31T09:15:28Z)
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.