MAX-R
MAX-R, the Mixed Augmented and Extended Reality Media Pipeline, aims to define, develop, validate and demonstrate a complete pipeline of tools for making, processing and delivering maximum-quality XR content. The pipeline will be based on open APIs, open file and data transfer formats, which enable the development and support the integration of new tools. We will build on research and advances in VP technologies to develop real-time processes and tools that deliver better quality, greater efficiency, ease of use and interoperability between proprietary and open-source software. The new tools and processes will deliver enhanced interactivity and novel content based on XR media data.
A real-time pipeline based on open standards, creating pathways to further innovation, will launch a new era in the production of XR media.
The MAX-R consortium is led by Universitat Pompeu Fabra and includes the following partners:
Further information can be obtained from the official website: MAX-R.EU
This project has received funding from the European Union's Horizon Europe Research and Innovation Programme under Grant Agreement No 101070072.
Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Health and Digital Executive Agency. Neither the European Union nor the granting autority can be held responsible for them.
DIGITAL LOCATIONS
The DIGITAL LOCATIONS demonstrator exemplifies the advanced virtualisation of shooting locations for film productions. It showcases the successful application of digital replicas of real-world locations for previsualisation, shot planning under physical constraints and full Virtual Production. Additionally, the concept of DIGITAL LOCATIONS enables the virtual and collaborative exploration of buildings, heritage sites and natural landmarks, which can significantly reduce costs and, more importantly, support the Green Deal by minimising the need for travel.
The demonstrator, currently under development, utilises core MAX-R technologies developed by Filmakademie Baden-Württemberg VPET, TRACER, DataHub) and UPF (wgpuEngine). This project was created in close collaboration with members of the MAX-R Creative User Group: Film Commission Region Stuttgart and Third Picture GBR.
Updates on the publicly available demonstrator, expected by early 2025.
As part of the DIGITAL LOCATIONS initiative, we have released an initial dataset of the CONTAINER CITY STUTTGART under the Creative Commons licence.
SHORT FILM SURVIVOR
In MAX-R, we are developing AI-assisted software tools and pipelines designed to streamline and enhance workflows for 3D animated film productions. These tools aim to empower filmmakers and animators to create bipedal character animations in real-time with the aid of the latest advances in machine learning.
AnimHost, an integral component of the TRACER ecosystem, addresses these challenges by connecting animation generators to Digital Content Creation (DCC) applications and other clients. The generated animations can be exported, further processed and refined in standard DCC applications such as the open-source software Blender. This integration allows animation artists to remain within their familiar creation environments while utilising machine learning capabilities and maintaining ultimate creative control over their content.
To test, evaluate and visualise this workflow in a realistic production environment, we are currently producing an animated short film titled SURVIVOR:
Set in a dystopian, desolate world of rusted metal, the film follows a lonely old robot racing towards an unknown destination. Pursued by an all-consuming red fog, he climbs a massive mountain to protect his precious cargo. Faced with an insurmountable obstacle and the danger closing in rapidly behind him, he must make a critical decision to ensure his survival.
SURVIVOR ANIMHOST MOTION CAPTURE - DATASET
The training data for the AnimHost animation generator was developed through a dedicated motion capture session at the Filmakademie Baden-Württemberg, ensuring fair use and traceability of ownership.
This dataset has been released under the Creative Commons licence by Filmakademie Baden-Württemberg.