David Berga 👨‍💻🎞️🖖🛰️🖼️✍

https://enti.cat/

Del Programa RETECH (2025) - Línia 3 Activitats de dinamització de l’ecosistema d’entitats que desenvolupen i integren, implementen o ofereixen solucions tecnològiques en l’àmbit de la IA.

Tanto el sector del hábitat como el de la moda comparten una serie de retos creativos y productivos en los que la inteligencia artificial generativa puede ofrecer soluciones transformadoras. Ambas industrias se encuentran en un punto de inflexión, donde la tecnología ya no solo mejora la eficiencia, sino que redefine los procesos creativos y de diseño, permitiendo nuevas formas de imaginar, producir y comunicar.

El proyecto CreativIA tiene como objetivo acercar el potencial de la IA generativa a las industrias creativas, especialmente a los sectores del hábitat y la moda, a través de la transferencia de conocimiento, la creación de comunidad y la experimentación práctica con tecnologías emergentes. Mediante acciones de scouting, jornadas formativas, talleres y actividades colaborativas, el proyecto busca democratizar el acceso a la IA y fomentar la co-creación entre profesionales del diseño y expertos en inteligencia artificial. De este modo, el proyecto contribuirá a fortalecer el tejido productivo catalán, impulsando la digitalización y posicionando la industria creativa del diseño de Catalunya como territorio pionero en la fusión entre creatividad e inteligencia artificial.

INSIDE-Act: UnconventIonal and traNsdiSciplinary traIning among DEsign, Art, soCial sciences and digital for shaping a new immersive sTorytelling designer

This project stems from a deep reflection on the recent digital trends of the market, on the growing gap of skills in digital communication and on the emergence of the current social, environmental and economic challenges. In relation to this, the main goal of INSIDE-ACT is to develop the profile of an Immersive Storytelling Designer (ISD), able to mix methods and tools typical of Design, Performing Arts, Social sciences and Digital (DASD) in order to be able to design a strategic immersive digital communication (XR), with a view to raising awareness of EU values. Within the DASD, performing art will play a disruptive role, providing the tools to overcome disciplinary limits and build a new transdisciplinary-informal, almost “indisciplinary” methodology. To achieve this goal, INSIDE-ACT develops an innovative training package built thanks to the alliance - stimulated since the early stages of the project - among HEIs, VETs, sector representatives, clusters, digital companies and CCIs. The INSIDE-ACT training package consists of 6 modules that develop a direct training experience between companies (traditional, digital and creative) and students. The companies are an active part of the training modules and driving forces of the final intensive workshop which will lead to the creation of immersive storytelling executive experiences based on the concepts produced by the groups of learners (composed of university students and professionals from the various sectors of the DASD). This training model - as well as all the activities and outputs developed in INSIDE-ACT - will clearly show the transformative potential of strategic immersive storytelling which, in addition to disseminating European values, sensitises the community by stimulating virtuous behaviours (Transformative Design). In this sense, the ISD profile will play the role of trigger of a sustainable, inclusive and critical innovation that will disruptively fit into the imminent demands of the labour market.

ELISAVA participates mainly through WP2 (Setting up the Immersive Storytelling Designer ISD profiles), led by Manuela Valtchanova, Guim Espelt and Saúl Baeza.

https://www.unifi.it/en/university/around-world/erasmus-and-international-mobility

https://www.ricercaeinnovazione.it/fund/erasmus-edu-2025-pi-all-inno-edu-enterp-alliances-for-education-and-enterprises/11514

Specularity in NeRFs: A Comparative Study of Ref-NeRF and NRFF

Neural Radiance Fields (NeRF) have emerged as a leading technology for 3D digitization, especially for their high accuracy and intricate detailing. Despite their advancements, early NeRF models struggle to handle reflections on specular surfaces effectively. To address this, alternative approaches such as Ref-NeRF and NRFF were proposed to improve fidelity in representing this physical phenomenon. This study compares these two models, providing an analysis of their effectiveness and limitations in dealing with complex specularities. We demonstrate that both methods struggle with inter-reflections and tend to model anisotropic specularities by altering the predicted surface normals.

Participating in the new EP of Heilan & the Flourescent Band named "Metanoia", check out the singles in Spotify:

The Stress Is Real: Physiological Measurement of League of Legends Players Experience During a Live Esports Event Videogames and Esports experienced a huge growth in popularity lately and have opened a ripe new field for the study of human behavior. Esports gaming is an area in which videogame players need to cooperate and compete with each other, influencing their cognitive load, processing, stress, and as well as social skills. In this observational study we inquire whether variations in autonomic nervous system activity can be obtained reliably during a live League of Legends (LoL) event, especially considering this is a preliminary study with a limited participant sample. We found that game performance (winning or losing the game) significantly affects electrodermal activity and cardiac modulation, where players who lost the game showed higher stress-related physiological responses, compared to players who won. We also found that specific important events in the game, such as "Killing," "Dying," or "Destroying the turret," increased players' electrodermal and cardiac modulation compared with other less relevant events, such as "Placing the guards" or "Destroying the turret plates." Finally, by analyzing activity according to players' roles, we found various notable activity trends. Altogether, these (yet preliminary) results encourage further exploration of physiology-based applications for LoL and Esports players on live events.

Designed my own cards and infographics for supporting character creation, rules and manuals for Dungeons and Dragons 5e in Tabletop Simulator. Includes dices, book (free d&d 5e ver.) and a fully-scripted & editable PDF for character creation. Download the mods in Steam:

https://www.elisava.net/en/

EDGEAIMA: Estrategia Digital de Gestión Empática en Asistencia Inteligente y Medicina Personalizada

https://github.com/dberga/nerfstudio Available methods here https://docs.nerf.studio/nerfology/methods/index.html

See my contributions here: https://github.com/nerfstudio-project/nerfstudio/graphs/contributors https://github.com/nerfstudio-project/nerfstudio/commits?author=dberga https://github.com/nerfstudio-project/nerfstudio/issues?q=is%3Aissue%20state%3Aclosed%20author%3Adberga Mainly helped photogrammetry runs for distinct size/camera properties and some i/o implementation.

Visit my channel here: https://www.twitch.tv/pcgamintv/about

https://ieeexplore.ieee.org/document/10356628

A New Framework for Evaluating Image Quality Including Deep Learning Task Performances as A Proxy

iquaflow is a framework that provides a set of tools to assess image quality. The user can add custom metrics that can be easily integrated and a set of unsupervised methods is offered by default. Furthermore, iquaflow measures quality by using the performance of AI models trained on the images as a proxy. This also helps to easily make studies of performance degradation of several modifications of the original dataset, for instance, with images reconstructed after different levels of lossy compression; satellite images would be a use case example, since they are commonly compressed before downloading to the ground. In this situation, the optimization problem involves finding images that, while being compressed to their smallest possible file size, still maintain sufficient quality to meet the required performance of the deep learning algorithms. Thus, a study with iquaflow is suitable for such case. All this development is wrapped in Mlflow : an interactive tool used to visualize and summarize the results. This document describes different use cases and provides links to their respective repositories. To ease the creation of new studies, we include a cookiecutter repository. The source code, issue tracker and aforementioned repositories are all hosted on GitHub.

I have developed new mods for Kenshi (using the object-oriented tools from the Forgotten Construction Set) and integrating Ogre's meshes and textures with Blender. Check mods in Nexus and Steam: https://next.nexusmods.com/profile/dber43/mods?gameId=736 https://steamcommunity.com/id/dber/myworkshopfiles/?appid=233860

PHASE IV AI: Privacy Compliant Health Data As A Service For AI Development

https://www.mdpi.com/2072-4292/15/9/2451 QMRNet: Quality Metric Regression for EO Image Quality Assessment and Super-Resolution

The latest advances in super-resolution have been tested with general-purpose images such as faces, landscapes and objects, but mainly unused for the task of super-resolving earth observation images. In this research paper, we benchmark state-of-the-art SR algorithms for distinct EO datasets using both full-reference and no-reference image quality assessment metrics. We also propose a novel Quality Metric Regression Network (QMRNet) that is able to predict the quality (as a no-reference metric) by training on any property of the image (e.g., its resolution, its distortions, etc.) and also able to optimize SR algorithms for a specific metric objective. This work is part of the implementation of the framework IQUAFLOW, which has been developed for the evaluation of image quality and the detection and classification of objects as well as image compression in EO use cases. We integrated our experimentation and tested our QMRNet algorithm on predicting features such as blur, sharpness, snr, rer and ground sampling distance and obtained validation medRs below 1.0 (out of N = 50) and recall rates above 95%. The overall benchmark shows promising results for LIIF, CAR and MSRN and also the potential use of QMRNet as a loss for optimizing SR predictions. Due to its simplicity, QMRNet could also be used for other use cases and image domains, as its architecture and data processing is fully scalable.

-IQUAFLOW https://github.com/satellogic/iquaflow -IQUAFLOW-Modifiers: https://github.com/satellogic/iquaflow/tree/main/iquaflow/datasets - QMRNet https://github.com/satellogic/iquaflow/tree/main/iquaflow/quality_ metrics