Generative Adversarial Networks (GANs) have emerged as powerful tools not only for high-quality image generation but also for real image editing through manipulation of their interpretable latent spaces. Recent advancements in GANs include the development of 3D-aware models such as EG3D, characterized by efficient triplane-based architectures enabling the reconstruction of 3D geometry from single images. However, scant attention has been devoted to providing an integrated framework for high-quality reference-based 3D-aware image editing within this domain. This study addresses this gap by exploring and demonstrating the effectiveness of EG3D's triplane space for achieving advanced reference-based edits, presenting a unique perspective on 3D-aware image editing through our novel pipeline. Our approach integrates the encoding of triplane features, spatial disentanglement and automatic localization of features in the triplane domain, and fusion learning for desired image editing. Moreover, our framework demonstrates versatility across domains, extending its effectiveness to animal face edits and partial stylization of cartoon portraits. The method shows significant improvements over relevant 3D-aware latent editing and 2D reference-based editing methods, both qualitatively and quantitatively. Project page: https://three-bee.github.io/triplane_edit

pdf/reference-based-3d-aware-image-editing-with-triplane-16ftzhpjqp.pdf

Reference-Based 3D-Aware Image Editing with Triplane

Neighborhood Contrastive Learning-based Graph Neural Network for Bug Triaging

Generative Adversarial Networks (GANs) enable image editing by manipulating image features. However, these manipulations still lack disentanglement. For example, when a specific wrinkle is edited, other age-related features or facial expressions are often changed as well. This paper proposes a new method for disentangled editing. The presented approach is based on two pivot images that allow learning an editing direction for an input image. These pivots are based on a real image (the input) and a synthetic modification of the real image along the desired editing direction. Although our primary focus is on wrinkle editing applications, our method can be extended to other editing tasks, such as hair color or lipstick editing. Qualitative and quantitative results show that our Pivotal Tuning Editing (PTE) provides a higher level of disentanglement and a more realistic editing than state-of-the-art methods. The code is available on GitHub1.

Pivotal Tuning Editing: Towards Disentangled Wrinkle Editing with GANs

Abstract Improving the accuracy and disentanglement of attribute translation, and maintaining the consistency of face identity have been hot topics in face attribute translation. Recent approaches employ attention mechanisms to enable attribute translation in facial images. However, due to the lack of accuracy in the extraction of style code, the attention mechanism alone is not precise enough for the translation of attributes. To tackle this, we introduce a style translator module, which partitions the style code into attribute‐related and unrelated components, enhancing latent space disentanglement for more accurate attribute manipulation. Additionally, many current methods use per‐pixel loss functions to preserve face identity. However, this can sacrifice crucial high‐level features and textures in the target image. To address this limitation, we propose a multiple‐perceptual reconstruction loss to better maintain image fidelity. Extensive qualitative and quantitative experiments in this article demonstrate significant improvements over state‐of‐the‐art methods, validating the effectiveness of our approach. 

Face attribute translation with multiple feature perceptual reconstruction assisted by style translator

Findable. Accessible. Interoperable. Reusable. Since their introduction in 2016, the FAIR data principles have defined the standards by which scientific researchers share data. However, modern research in data editing and management consistently shows that while the FAIR data principles are widely accepted in theory, they can be much more difficult to understand and implement in practice. In this tutorial, we explore some of the simple, realistic steps scientists can take to FAIRly release open data. We also explore areas where the current FAIR guidelines fall short and offer practical suggestions for making open data FAIR(ER): more Equitable and Realistic. This first involves ways to make datasets themselves more equitably accessible for researchers with disabilities. While equitably accessible data design has some research overlap with paper, presentation, and website design, we suggest several unique distinctions specific to datasets. The "Realistic'' aspect of FAIR(ER) data facilitates a path to translate open data (and research on that data) back to true applications. Driven by national security applications pipelines, we call out important considerations for balancing data editing against data realism. 

Sharing is Caring: A Practical Guide to FAIR(ER) Open Data Release

We propose an image-to-image translation framework for facial attribute editing with disentangled interpretable latent directions. Facial attribute editing task faces the challenges of targeted attribute editing with controllable strength and disentanglement in the representations of attributes to preserve the other attributes during edits. For this goal, inspired by the latent space factorization works of fixed pretrained GANs, we design the attribute editing by latent space factorization, and for each attribute, we learn a linear direction that is orthogonal to the others. We train these directions with orthogonality constraints and disentanglement losses. To project images to semantically organized latent spaces, we set an encoder-decoder architecture with attention-based skip connections. We extensively compare with previous image translation algorithms and editing with pretrained GAN works. Our extensive experiments show that our method significantly improves over the state-of-the-arts. 

pdf/image-to-image-translation-with-disentangled-latent-vectors-2i7xnxpbrf.pdf

Image-to-Image Translation With Disentangled Latent Vectors for Face Editing

—We propose an image-to-image translation framework for facial attribute editing with disentangled interpretable latent directions. Facial attribute editing task faces the challenges of targeted attribute editing with controllable strength and disentanglement in the representations of attributes to preserve the other attributes during edits. For this goal, inspired by the latent space factorization works of ﬁxed pretrained GANs, we design the attribute editing by latent space factorization, and for each attribute, we learn a linear direction that is orthogonal to the others. We train these directions with orthogonality constraints and disentanglement losses. To project images to semantically organized latent spaces, we set an encoder-decoder architecture with attention-based skip connections. We extensively compare with previous image translation algorithms and editing with pretrained GAN works. Our extensive experiments show that our method signiﬁcantly improves over the state-of-the-arts. Project page: https://yusufdalva.github.io/vecgan

Face Attribute Editing with Disentangled Latent Vectors

In the context of collaborative software development, there are many application areas of task assignment such as assigning a developer to fix a bug, or assigning a code reviewer to a pull request. Most task assignment techniques in the literature build and evaluate their models based on datasets collected from real projects. The techniques invariably presume that these datasets reliably represent the “ground truth”. In a project dataset used to build an automated task assignment system, the recommended assignee for the task is usually assumed to be the best assignee for that task. However, in practice, the task assignee may not be the best possible task assignee, or even a sufficiently qualified one. We aim to clean up the ground truth by removing the samples that are potentially problematic or suspect with the assumption that removing such samples would reduce any systematic labeling bias in the dataset and lead to performance improvements. We devised a debiasing method to detect potentially problematic samples in task assignment datasets. We then evaluated the method’s impact on the performance of seven task assignment techniques by comparing the Mean Reciprocal Rank (MRR) scores before and after debiasing. We used two different task assignment applications for this purpose: Code Reviewer Recommendation (CRR) and Bug Assignment (BA). In the CRR application, we achieved an average MRR improvement of 18.17% for the three learning-based techniques tested on two datasets. No significant improvements were observed for the two optimization-based techniques tested on the same datasets. In the BA application, we achieved a similar average MRR improvement of 18.40% for the two learning-based techniques tested on four different datasets. Debiasing the ground truth data by removing suspect samples can help improve the performance of learning-based techniques in software task assignment applications. • Devised a debiasing method to clean task assignment datasets. • Conducted experiments in two task assignment applications. • Debiasing the ground truth data improves learning-based techniques’ performance. 

Cleaning ground truth data in software task assignment

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Image-to-Image Translation With Disentangled Latent Vectors for Face Editing

Face Attribute Editing with Disentangled Latent Vectors

Cleaning ground truth data in software task assignment