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1. Modeling Aesthetics and Emotions in Visual Content: From Vincent van Gogh to Robotics and Vision

   Wang, James Z. (October 2020, Proceedings of the Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends, in conjunction with the ACM International Conference on Multimedia)

   null (Ed.)

   As inborn characteristics, humans possess the ability to judge visual aesthetics, feel the emotions from the environment, and comprehend others’ emotional expressions. Many exciting applications become possible if robots or computers can be empowered with similar capabilities. Modeling aesthetics, evoked emotions, and emotional expressions automatically in unconstrained situations, however, is daunting due to the lack of a full understanding of the relationship between low-level visual content and high-level aesthetics or emotional expressions. With the growing availability of data, it is possible to tackle these problems using machine learning and statistical modeling approaches. In the talk, I provide an overview of our research in the last two decades on data-driven analyses of visual artworks and digital visual content for modeling aesthetics and emotions. First, I discuss our analyses of styles in visual artworks. Art historians have long observed the highly characteristic brushstroke styles of Vincent van Gogh and have relied on discerning these styles for authenticating and dating his works. In our work, we compared van Gogh with his contemporaries by statistically analyzing a massive set of automatically extracted brushstrokes. A novel extraction method is developed by exploiting an integration of edge detection and clustering-based segmentation. Evidence substantiates that van Gogh’s brushstrokes are strongly rhythmic. Next, I describe an effort to model the aesthetic and emotional characteristics in visual contents such as photographs. By taking a data-driven approach, using the Internet as the data source, we show that computers can be trained to recognize various characteristics that are highly relevant to aesthetics and emotions. Future computer systems equipped with such capabilities are expected to help millions of users in unimagined ways. Finally, I highlight our research on automated recognition of bodily expression of emotion. We propose a scalable and reliable crowdsourcing approach for collecting in-the-wild perceived emotion data for computers to learn to recognize the body language of humans. Comprehensive statistical analysis revealed many interesting insights from the dataset. A system to model the emotional expressions based on bodily movements, named ARBEE (Automated Recognition of Bodily Expression of Emotion), has also been developed and evaluated. 

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2. The Photographer's Eye: Teaching Multimodal Large Language Models to See, and Critique Like Photographers

   Qi, Daiqing; Zhao, Handong; Shi, Jing; Jenni, Simon; Fan, Yifei; Dernoncourt, Franck; Cohen, Scott; Li, Sheng (June 2025, The IEEE/CVF Conference on Computer Vision and Pattern Recognition)

   Photographer, curator, and former director of photography at the Museum of Modern Art (MoMA), John Szarkowski remarked in *William Eggleston's Guide*, "While editing directly from life, photographers have found it too difficult to see simultaneously both the blue and the sky." Szarkowski insightfully revealed a notable gap between general and aesthetic visual understanding: while the former emphasizes identifying factual elements in an image (the sky), the latter transcends mere object identification, viewing it instead as an aesthetic component--a pure expanse of blue, valued purely as a color block in visual aesthetics. Such distinctions between general visual understanding (detection, localization, etc.) and aesthetic perception (color, lighting, composition, etc.) pose a significant challenge for existing Multimodal Large Language Models (MLLMs) in comprehending image aesthetics, which is increasingly needed in real-world applications, from image recommendation and enhancement to generation. To fundamentally advance the aesthetic understanding of MLLMs, we introduce a novel dataset, PhotoCritique, derived from extensive discussions among professional photographers and enthusiasts, distinguished by its large scale, expertise, and diversity. Additionally, we propose a new model, PhotoEye, an MLLM featuring a language-guided multi-view vision fusion mechanism for understanding image aesthetics from multiple perspectives. Finally, we introduce PhotoBench, a comprehensive and professional benchmark for aesthetic visual understanding. Our model demonstrates significant advantages over both open-source and commercial models on existing benchmarks and PhotoBench. 

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3. Autonomous Aesthetic Views Finding in Indoor Scenes with Deep Reinforcement Learning

   Xie, D.; Hu, P.; Sun, X.; Pirk, S.; Zhang, J.; Mech, R.; Kaufman, A. E. (October 2023, International Conference on Computer Vision)

   Placing and orienting a camera to compose aesthetically meaningful shots of a scene is not only a key objective in real-world photography and cinematography but also for virtual content creation. The framing of a camera often significantly contributes to the story telling in movies, games, and mixed reality applications. Generating single camera poses or even contiguous trajectories either requires a significant amount of manual labor or requires solving highdimensional optimization problems, which can be computationally demanding and error-prone. In this paper, we introduce GAIT, a Deep Reinforcement Learning (DRL) agent, that learns to automatically control a camera to generate a sequence of aesthetically meaningful views for synthetic 3D indoor scenes. To generate sequences of frames with high aesthetic value, GAIT relies on a neural aesthetics estimator, which is trained on a crowed-sourced dataset. Additionally, we introduce regularization techniques for diversity and smoothness to generate visually interesting trajectories for a 3D environment, and to constrain agent acceleration in the reward function to generate a smooth sequence of camera frames. We validated our method by comparing it to baseline algorithms, based on a perceptual user study, and through ablation studies. The source code of our method will be released with the final version of our paper. 

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4. Sparsity in an artificial neural network predicts beauty: Towards a model of processing-based aesthetics

   https://doi.org/10.1371/journal.pcbi.1011703  

   Dibot, Nicolas M; Tieo, Sonia; Mendelson, Tamra C; Puech, William; Renoult, Julien P (December 2023, PLOS Computational Biology)

   Fleming, Roland W (Ed.)

   Generations of scientists have pursued the goal of defining beauty. While early scientists initially focused on objective criteria of beauty (‘feature-based aesthetics’), philosophers and artists alike have since proposed that beauty arises from the interaction between the object and the individual who perceives it. The aesthetic theory of fluency formalizes this idea of interaction by proposing that beauty is determined by the efficiency of information processing in the perceiver’s brain (‘processing-based aesthetics’), and that efficient processing induces a positive aesthetic experience. The theory is supported by numerous psychological results, however, to date there is no quantitative predictive model to test it on a large scale. In this work, we propose to leverage the capacity of deep convolutional neural networks (DCNN) to model the processing of information in the brain by studying the link between beauty and neuronal sparsity, a measure of information processing efficiency. Whether analyzing pictures of faces, figurative or abstract art paintings, neuronal sparsity explains up to 28% of variance in beauty scores, and up to 47% when combined with a feature-based metric. However, we also found that sparsity is either positively or negatively correlated with beauty across the multiple layers of the DCNN. Our quantitative model stresses the importance of considering how information is processed, in addition to the content of that information, when predicting beauty, but also suggests an unexpectedly complex relationship between fluency and beauty. 

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5. DeepGD: A Deep Learning Framework for Graph Drawing Using GNN

   Xiaoqi Wang, Kevin Yen (September 2021, IEEE computer graphics and applications)

   Abstract—In the past decades, many graph drawing techniques have been proposed for generating aesthetically pleasing graph layouts. However, it remains a challenging task since different layout methods tend to highlight different characteristics of the graphs. Recently, studies on deep learning based graph drawing algorithm have emerged but they are often not generalizable to arbitrary graphs without re-training. In this paper, we propose a Convolutional Graph Neural Network based deep learning framework, DeepGD, which can draw arbitrary graphs once trained. It attempts to generate layouts by compromising among multiple pre-specified aesthetics considering a good graph layout usually complies with multiple aesthetics simultaneously. In order to balance the trade-off, we propose two adaptive training strategies which adjust the weight factor of each aesthetic dynamically during training. The quantitative and qualitative assessment of DeepGD demonstrates that it is capable of drawing arbitrary graphs effectively, while being flexible at accommodating different aesthetic criteria. 

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