Recovering Faces from Portraits with Auxiliary Facial Attributes

Recovering a photorealistic face from an artistic portrait is a challenging task since crucial facial details are often distorted or completely lost in artistic compositions. To handle this loss, we propose an Attribute-guided Face Recovery from Portraits (AFRP) that utilizes a Face Recovery Network (FRN) and a Discriminative Network (DN). FRN consists of an autoencoder with residual block-embedded skip-connections and incorporates facial attribute vectors into the feature maps of input portraits at the bottleneck of the autoencoder. DN has multiple convolutional and fully-connected layers, and its role is to enforce FRN to generate authentic face images with corresponding facial attributes dictated by the input attribute vectors. %Leveraging on the spatial transformer networks, FRN automatically compensates for misalignments of portraits. % and generates aligned face images. For the preservation of identities, we impose the recovered and ground-truth faces to share similar visual features. Specifically, DN determines whether the recovered image looks like a real face and checks if the facial attributes extracted from the recovered image are consistent with given attributes. %Our method can recover high-quality photorealistic faces from unaligned portraits while preserving the identity of the face images as well as it can reconstruct a photorealistic face image with a desired set of attributes. Our method can recover photorealistic identity-preserving faces with desired attributes from unseen stylized portraits, artistic paintings, and hand-drawn sketches. On large-scale synthesized and sketch datasets, we demonstrate that our face recovery method achieves state-of-the-art results.Comment: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV

The influence of emotional priming on the neural substrates of memory: a prospective fMRI study using portrait art stimuli

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Two Paths to Abstract Art: Kandinsky and Malevich

Wassily Kandinsky and Kazimir Malevich were both great Russian painters who became pioneers of abstract art during the second decade of the twentieth century. Yet the forms of their art differed radically, as did their artistic methods and goals. Kandinsky, an experimental artist, approached abstraction tentatively and visually, by gradually and progressively concealing forms drawn from nature, whereas Malevich, a conceptual innovator, plunged precipitously into abstraction, by creating symbolic elements that had no representational origins. The conceptual Malevich also made his greatest innovations considerably earlier in his life than the experimental Kandinsky. Interestingly, at the age of 50 Kandinsky wrote an essay that clearly described these two categories of artist, contrasting the facile and protean young virtuoso with the single-minded individual who matured more slowly but was ultimately more original.

Context-Aware Embeddings for Automatic Art Analysis

Automatic art analysis aims to classify and retrieve artistic representations from a collection of images by using computer vision and machine learning techniques. In this work, we propose to enhance visual representations from neural networks with contextual artistic information. Whereas visual representations are able to capture information about the content and the style of an artwork, our proposed context-aware embeddings additionally encode relationships between different artistic attributes, such as author, school, or historical period. We design two different approaches for using context in automatic art analysis. In the first one, contextual data is obtained through a multi-task learning model, in which several attributes are trained together to find visual relationships between elements. In the second approach, context is obtained through an art-specific knowledge graph, which encodes relationships between artistic attributes. An exhaustive evaluation of both of our models in several art analysis problems, such as author identification, type classification, or cross-modal retrieval, show that performance is improved by up to 7.3% in art classification and 37.24% in retrieval when context-aware embeddings are used

Identity-preserving Face Recovery from Portraits

Recovering the latent photorealistic faces from their artistic portraits aids human perception and facial analysis. However, a recovery process that can preserve identity is challenging because the fine details of real faces can be distorted or lost in stylized images. In this paper, we present a new Identity-preserving Face Recovery from Portraits (IFRP) to recover latent photorealistic faces from unaligned stylized portraits. Our IFRP method consists of two components: Style Removal Network (SRN) and Discriminative Network (DN). The SRN is designed to transfer feature maps of stylized images to the feature maps of the corresponding photorealistic faces. By embedding spatial transformer networks into the SRN, our method can compensate for misalignments of stylized faces automatically and output aligned realistic face images. The role of the DN is to enforce recovered faces to be similar to authentic faces. To ensure the identity preservation, we promote the recovered and ground-truth faces to share similar visual features via a distance measure which compares features of recovered and ground-truth faces extracted from a pre-trained VGG network. We evaluate our method on a large-scale synthesized dataset of real and stylized face pairs and attain state of the art results. In addition, our method can recover photorealistic faces from previously unseen stylized portraits, original paintings and human-drawn sketches

Free-hand sketch recognition by multi-kernel feature learning

Abstract Free-hand sketch recognition has become increasingly popular due to the recent expansion of portable touchscreen devices. However, the problem is non-trivial due to the complexity of internal structures that leads to intra-class variations, coupled with the sparsity in visual cues that results in inter-class ambiguities. In order to address the structural complexity, a novel structured representation for sketches is proposed to capture the holistic structure of a sketch. Moreover, to overcome the visual cue sparsity problem and therefore achieve state-of-the-art recognition performance, we propose a Multiple Kernel Learning (MKL) framework for sketch recognition, fusing several features common to sketches. We evaluate the performance of all the proposed techniques on the most diverse sketch dataset to date (Mathias et al., 2012), and offer detailed and systematic analyses of the performance of different features and representations, including a breakdown by sketch-super-category. Finally, we investigate the use of attributes as a high-level feature for sketches and show how this complements low-level features for improving recognition performance under the MKL framework, and consequently explore novel applications such as attribute-based retrieval

Show, don't tell: Non-verbal eyewitness testimony based on non-artistic face sketches

Ph.DDOCTOR OF PHILOSOPH

Insights from a digital diary: exploring the creative process of the game-installation In[The Hate Booth]

This article proposes an approach to the creative gesture, particularly in digital art, and the methodologies that provide access to the process of creation. It suggests a study on the development of a digital diary to document and reflect on the creative process, with a specific focus on the In[The Hate Booth] project. Through analysing entries in the project's digital diary, the research explores the artistic process and the development of the artwork. The findings highlight how digital diaries may ease idea exploration, the conceptual breakthroughs, and critical insights of the creative work, also emphasizing the importance of documenting the process for digital preservation.info:eu-repo/semantics/publishedVersio