Camera virtualization -- an emerging solution to novel view synthesis -- holds transformative potential for visual entertainment, live performances, and sports broadcasting by enabling the generation of photorealistic images from novel viewpoints using images from a limited set of calibrated multiple static physical cameras. Despite recent advances, achieving spatially and temporally coherent and photorealistic rendering of dynamic scenes with efficient time-archival capabilities, particularly in fast-paced sports and stage performances, remains challenging for existing approaches. Recent methods based on 3D Gaussian Splatting (3DGS) for dynamic scenes could offer real-time view-synthesis results. Yet, they are hindered by their dependence on accurate 3D point clouds from the structure-from-motion method and their inability to handle large, non-rigid, rapid motions of different subjects (e.g., flips, jumps, articulations, sudden player-to-player transitions). Moreover, independent motions of multiple subjects can break the Gaussian-tracking assumptions commonly used in 4DGS, ST-GS, and other dynamic splatting variants. This paper advocates reconsidering a neural volume rendering formulation for camera virtualization and efficient time-archival capabilities, making it useful for sports broadcasting and related applications. By modeling a dynamic scene as rigid transformations across multiple synchronized camera views at a given time, our method performs neural representation learning, providing enhanced visual rendering quality at test time. A key contribution of our approach is its support for time-archival, i.e., users can revisit any past temporal instance of a dynamic scene and can perform novel view synthesis, enabling retrospective rendering for replay, analysis, and archival of live events, a functionality absent in existing neural rendering approaches and novel view synthesis...
Wystawa Wielkopolska plastyka gotycka, zorganizowana w 1936 r. w Muzeum Wielkopolskim, była jednym z najważniejszych wydarzeń kulturalnych międzywojennego Poznania, dotąd nigdy niepowtórzonym w takiej skali. Odnalezione materiały archiwalne (wycinki prasowe, fotografie i dokumentacja) umożliwiają analizę samej ekspozycji oraz jej społecznego odbioru. Źródła te pozwalają również na rekonstrukcję procesu organizacyjnego oraz układu przestrzennego wystawy. Stanowią punkt wyjścia do refleksji nad nowymi perspektywami, jakie wystawa wniosła do badań nad gotycką sztuką Wielkopolski, oraz do uchwycenia jej wyróżników na tle innych prezentacji sztuki średniowiecznej w okresie międzywojennym. Przyjęta w artykule perspektywa metahistorii sztuki, koncentrująca się na paradygmacie narodowym, pozwala osadzić wystawę w szerszym, ideologicznie nacechowanym i nasyconym nacjonalistycznymi treściami dyskursie toczącym się w ramach historii sztuki w XX w.
The rise of photographic publishing in France appeared at the time of the emergence of photographer associations : the Faut Voir agency (1982-2000) and the bar Floréal collective (1985-2015), themselves producers and distributors of photographic books. This analysis allows us to understand the relationship between several uses of photography and the role of publishing, based on a few exemplary achievements.
Foundation models (FMs) have shown transformative potential in radiology by performing diverse, complex tasks across imaging modalities. Here, we developed CT-FM, a large-scale 3D image-based pre-trained model designed explicitly for various radiological tasks. CT-FM was pre-trained using 148,000 computed tomography (CT) scans from the Imaging Data Commons through label-agnostic contrastive learning. We evaluated CT-FM across four categories of tasks, namely, whole-body and tumor segmentation, head CT triage, medical image retrieval, and semantic understanding, showing superior performance against state-of-the-art models. Beyond quantitative success, CT-FM demonstrated the ability to cluster regions anatomically and identify similar anatomical and structural concepts across scans. Furthermore, it remained robust across test-retest settings and indicated reasonable salient regions attached to its embeddings. This study demonstrates the value of large-scale medical imaging foundation models and by open-sourcing the model weights, code, and data, aims to support more adaptable, reliable, and interpretable AI solutions in radiology.
Diederik Aerts, Jonito Aerts Arguëlles, Lester Beltran
et al.
We present the results of cognitive tests on conceptual combinations, performed using specific Large Language Models (LLMs) as test subjects. In the first test, performed with ChatGPT and Gemini, we show that Bell's inequalities are significantly violated, which indicates the presence of 'quantum entanglement' in the tested concepts. In the second test, also performed using ChatGPT and Gemini, we instead identify the presence of 'Bose-Einstein statistics', rather than the intuitively expected 'Maxwell-Boltzmann statistics', in the distribution of the words contained in large-size texts. Interestingly, these findings mirror the results previously obtained in both cognitive tests with human participants and information retrieval tests on large corpora. Taken together, they point to the 'systematic emergence of quantum structures in conceptual-linguistic domains', regardless of whether the cognitive agent is human or artificial. Although LLMs are classified as neural networks for historical reasons, we believe that a more essential form of knowledge organization takes place in the distributive semantic structure of vector spaces built on top of the neural network. It is this meaning-bearing structure that lends itself to a phenomenon of evolutionary convergence between human cognition and language, slowly established through biological evolution, and LLM cognition and language, emerging much more rapidly as a result of self-learning and training. We analyze various aspects and examples that contain evidence supporting the above hypothesis. We also advance a unifying framework that explains the pervasive quantum organization of meaning that we identify.
We explore the effects of data and design considerations through the example case of part-to-whole data relationships. Standard part-to-whole representations like pie charts and stacked bar charts make the relationships of parts to the whole explicit. Value estimation in these charts benefits from two perceptual mechanisms: anchoring, where the value is close to a reference value with an easily recognized shape, and alignment where the beginning or end of the shape is aligned with a marker. In an online study, we explore how data and design factors such as value, position, and encoding together impact these effects in making estimations in part-to-whole charts. The results show how salient values and alignment to positions on a scale affect task performance. This demonstrates the need for informed visualization design based around how data properties and design factors affect perceptual mechanisms.
Guillaume Gautier, Alexandre Mercat, Louis Fréneau
et al.
Point cloud compression has become a crucial factor in immersive visual media processing and streaming. This paper presents a new open dataset called UVG-VPC for the development, evaluation, and validation of MPEG Visual Volumetric Video-based Coding (V3C) technology. The dataset is distributed under its own non-commercial license. It consists of 12 point cloud test video sequences of diverse characteristics with respect to the motion, RGB texture, 3D geometry, and surface occlusion of the points. Each sequence is 10 seconds long and comprises 250 frames captured at 25 frames per second. The sequences are voxelized with a geometry precision of 9 to 12 bits, and the voxel color attributes are represented as 8-bit RGB values. The dataset also includes associated normals that make it more suitable for evaluating point cloud compression solutions. The main objective of releasing the UVG-VPC dataset is to foster the development of V3C technologies and thereby shape the future in this field.
This work presents the Polynomiogram framework, an integrated computational platform for exploring, visualizing, and generating art from polynomial root systems. The main innovation is a flexible sampling scheme in which two independent parameters are drawn from user defined domains and mapped to the polynomial coefficients through a generating function. This design allows the same mathematical foundation to support both scientific investigation and generative algorithmic art. The framework integrates two complementary numerical engines: NumPy companion matrix solver for fast, large scale computation and MPSolve for high precision, scientifically rigorous validation. This dual architecture enables efficient visualization for creative use and accurate computation for research and education. Numerical accuracy was verified using classical ensembles, including the Kac and Lucas polynomials. The method was applied to the cubic polynomial system to analyze its bifurcation structure, demonstrating its value as both a scientific tool for exploring root phenomena and an educational aid for visualizing fundamental concepts in algebra and dynamical systems. Beyond analysis, the Polynomiogram also demonstrated its potential as a tool for personalized generative art. Examples include the use of the platform to generate a natural form resembling a hibiscus flower and to create personalized artwork expressing gratitude toward advances in artificial intelligence and large language models through a tribute composition.
Transness throws into question how many so-called Western cultures—i.e., those ideologically descended from the colonial project—have sutured “reality” to the “privileging of sight”. At the crux of trans-visual culture is a need to be understood outside current modes of visual apprehension. As a methodology rooted in trans-embodied experiences, trans provides a mode for decolonizing the privileging of sight and moving toward a new understanding of bodies, identity, representation, and visual culture. It is imperative to explore such methods in today’s political climate, and it is advantageous to apply them to trans-visual culture, as exponential innovations can be discerned. In this article, I will deploy a trans visual studies methodology to the work of contemporary trans masculine artist and photographer Wynne Neilly to explore how his work engages a praxis of transing identity. I will discuss how his work shifts the understanding of identity and representation to one decoupled from optical ontology and how he works to unseat White masculinity as the center of Western art and visual culture.
The concept of the inner child represents the emotional and experiential core of individuals, often shaped by early life experiences. This article focuses on research which explored the significance of play in awakening this inner child in black Indigenous African adults, particularly in the context of Drama Therapy. By utilizing methods such as neuro-dramatic play and guided play, this study seeks to understand how play can serve as a therapeutic tool to reconnect individuals with their past, promote healing, and enhance personal growth. The colonized African child growing up under post-colonial times may have the experience and memory of being deprived of play due to colonial factors that include Apartheid, land displacement, and but not limited to slave labour. A way to activate memories is through using the body as a vessel that allows the flow of experience to take place. This is why play is an important element of the research that informs this article as it assists one to understand how play can contribute to traveling to and navigating that space in time, using the body. The article aims to explore how play can awaken the inner child in black Indigenous African adults, contributing to the understanding of play within the context of Drama Therapy. I argue that the exploration of play can foster connections within communities and promote emotional healing, particularly in a post-apartheid South African context where historical traumas and socio-economic disparities persist. The study ultimately seeks to contribute to the field of Drama Therapy by emphasizing play as a vital process for self-discovery and emotional well-being.
The present text establishes a narrative of the filmmaking process by the authors of Facts of life. A film developed with 18 hours of video from Gilles Deleuze's courses at the University of Vincennes (1975-76). A working material taped by Marielle Burkhalter as part of her Master Project "film thoght in its becoming".
Este texto pone en relación la trayectoria del coreógrafo Steve Paxton con aspectos concretos del pensamiento de Adriana Cavarero y Donna Haraway. Paxton ha investigado durante décadas el cuerpo humano, a través del caminar, del estudio de la gravedad y de la realización del compost orgánico. A partir del concepto de inclinación desarrollado por Adriana Cavarero, en el que el yo al inclinarse se desestabiliza y se relaciona desde la vulnerabilidad, argumentaré que esta toma de consciencia permite re-experienciar nuestra relación con el cuerpo —y, por tanto, con el mundo—. Inclinarse, sentir la gravedad, imaginar, abren otras percepciones. Conforman una sensibilidad desde la que, al igual que en la elaboración del compost orgánico, sus microorganismos y microespecies, es posible afectar a una estructura mayor. Haraway afirma que somos compost, lo que nos lleva a situarnos más cerca de la tierra y a pensarnos como seres humanos, seres humus: espacios en los que los y las demás pueden crecer.
AI is the workhorse of modern data analytics and omnipresent across many sectors. Large Language Models and multi-modal foundation models are today capable of generating code, charts, visualizations, etc. How will these massive developments of AI in data analytics shape future data visualizations and visual analytics workflows? What is the potential of AI to reshape methodology and design of future visual analytics applications? What will be our role as visualization researchers in the future? What are opportunities, open challenges and threats in the context of an increasingly powerful AI? This Visualization Viewpoint discusses these questions in the special context of biomedical data analytics as an example of a domain in which critical decisions are taken based on complex and sensitive data, with high requirements on transparency, efficiency, and reliability. We map recent trends and developments in AI on the elements of interactive visualization and visual analytics workflows and highlight the potential of AI to transform biomedical visualization as a research field. Given that agency and responsibility have to remain with human experts, we argue that it is helpful to keep the focus on human-centered workflows, and to use visual analytics as a tool for integrating ``AI-in-the-loop''. This is in contrast to the more traditional term ``human-in-the-loop'', which focuses on incorporating human expertise into AI-based systems.
Generative AI (GenAI) has witnessed remarkable progress in recent years and demonstrated impressive performance in various generation tasks in different domains such as computer vision and computational design. Many researchers have attempted to integrate GenAI into visualization framework, leveraging the superior generative capacity for different operations. Concurrently, recent major breakthroughs in GenAI like diffusion model and large language model have also drastically increase the potential of GenAI4VIS. From a technical perspective, this paper looks back on previous visualization studies leveraging GenAI and discusses the challenges and opportunities for future research. Specifically, we cover the applications of different types of GenAI methods including sequence, tabular, spatial and graph generation techniques for different tasks of visualization which we summarize into four major stages: data enhancement, visual mapping generation, stylization and interaction. For each specific visualization sub-task, we illustrate the typical data and concrete GenAI algorithms, aiming to provide in-depth understanding of the state-of-the-art GenAI4VIS techniques and their limitations. Furthermore, based on the survey, we discuss three major aspects of challenges and research opportunities including evaluation, dataset, and the gap between end-to-end GenAI and generative algorithms. By summarizing different generation algorithms, their current applications and limitations, this paper endeavors to provide useful insights for future GenAI4VIS research.
The intersection between religious experience and aesthetic experience has become so obvious that the current “aesthetic turn” in Christian theology no longer needs to be defended. In this essay, I discuss that intersection point from the point of view of Roman Catholicism, in order to demonstrate the bold claim that the arts and the performance they evoke from us are as important as the creed for Catholicism. The essay aims to do three things: first, to examine that intersection point and emphasize the elements of intentionality and desire; second, to analyze one expression of that intersection, namely the connection among Catholic faith claims, the visual arts, and Catholicism’s incarnational-sacramental imagination (using depictions of the post-Resurrection Emmaus story); third, to use hints from Hartmut Rosa’s recent work on “resonance” to tease out how revelation and transformation occur at this intersection.
Review about Iván Rega Castro and Borja Franco Llopis´new book; Imágenes del islam y fiesta pública en la corte portuguesa. De la Unión Ibérica al terremoto de Lisboa, (Gijón: Ediciones Trea, 2021), 198 págs., (ISBN 978–84-18105–46 3).
Benjamin Bach, Mandy Keck, Fateme Rajabiyazdi
et al.
This paper is a call to action for research and discussion on data visualization education. As visualization evolves and spreads through our professional and personal lives, we need to understand how to support and empower a broad and diverse community of learners in visualization. Data Visualization is a diverse and dynamic discipline that combines knowledge from different fields, is tailored to suit diverse audiences and contexts, and frequently incorporates tacit knowledge. This complex nature leads to a series of interrelated challenges for data visualization education. Driven by a lack of consolidated knowledge, overview, and orientation for visualization education, the 21 authors of this paper-educators and researchers in data visualization-identify and describe 19 challenges informed by our collective practical experience. We organize these challenges around seven themes People, Goals & Assessment, Environment, Motivation, Methods, Materials, and Change. Across these themes, we formulate 43 research questions to address these challenges. As part of our call to action, we then conclude with 5 cross-cutting opportunities and respective action items: embrace DIVERSITY+INCLUSION, build COMMUNITIES, conduct RESEARCH, act AGILE, and relish RESPONSIBILITY. We aim to inspire researchers, educators and learners to drive visualization education forward and discuss why, how, who and where we educate, as we learn to use visualization to address challenges across many scales and many domains in a rapidly changing world: viseducationchallenges.github.io.
Elder Nora Marks Dauenhauer, Kheixwnéi, a poet and oral literary scholar and a mentor of the author, told the author “Life is a poem”. This essay will explore the ways in which the oral literary and visual arts of the Northwest Coast interact, how artists across multiple disciplines attain knowledge and develop as artists, and the ways in which the arts sing the poetry of Tlingit life. Examining the relationship between the arts will deepen one’s understanding of each art and illuminate how they inform and enrich one another.
Computational image aesthetic evaluation is a computable human aesthetic perception and judgment realized by machines, which has a significant impact on a variety of applications such as image advanced search and promotional exhibition of painting arts. Various approaches have been proposed in copious literature trying to solve this challenging problem. However, there have been few attempts in reviewing works from different types of visual arts, due to their significant differences in visual features and aesthetic principles. In this survey, we present a comprehensive listing of the reviewed works on aesthetic assessment of photographs and paintings, mainly highlighting the contributions and innovations of the existing approaches. We firstly introduce aesthetic assessment benchmark datasets in different categories. Then, conventional aesthetic evaluation approaches based on handcrafted features are reviewed. Besides, we systematically evaluate recent deep learning techniques that are useful for developing robust models for aesthetic prediction tasks in scoring, distribution, attribute, and description. Moreover, the possibility of aesthetic-aware color enhancement, recomposition of photo images, and automatic generation of aesthetic-guided art paintings through computational approaches are summarized. Finally, challenges and potential future directions for this field are discussed. We hope that our survey could serve as a comprehensive reference source for future research on computational aesthetics in visual media.
Since 2016, we have witnessed the tremendous growth of artificial intelligence+visualization (AI+VIS) research. However, existing survey papers on AI+VIS focus on visual analytics and information visualization, not scientific visualization (SciVis). In this paper, we survey related deep learning (DL) works in SciVis, specifically in the direction of DL4SciVis: designing DL solutions for solving SciVis problems. To stay focused, we primarily consider works that handle scalar and vector field data but exclude mesh data. We classify and discuss these works along six dimensions: domain setting, research task, learning type, network architecture, loss function, and evaluation metric. The paper concludes with a discussion of the remaining gaps to fill along the discussed dimensions and the grand challenges we need to tackle as a community. This state-of-the-art survey guides SciVis researchers in gaining an overview of this emerging topic and points out future directions to grow this research.