

relative.berlin
As relative.berlin, we’re a creative studio focused on developing distinctive animations and immersive experiences. Exploring emerging technologies (especially machine learning and AI) is a core part of how we work, and it continually shapes the way we approach new projects.
By integrating AI into our workflow, we were able to create rich, densely populated environments for VR and scale our creative ambitions in ways that would have been difficult with traditional pipelines alone. In the sections below, we share how we made it happen.
The Project: XR Security Lab
XR Security Lab is developed on behalf of the project Accompanying Research SifoLIFE: Effective and Sustainable in Practice (BeLIFE), which is funded by the Germany’s Federal Ministry of Research, Technology, and Space (BMFTR). For this initiative, we set out to create a VR experience that brings civil security research into a safe, repeatable virtual environment. To achieve this, we built five detailed urban scenarios, each representing complex situations such as extreme weather events or large-scale incidents. These virtual environments can be explored from multiple perspectives, making emergency procedures, response measures, and cause-and-effect relationships far easier to understand.



Marc-André Müller, Vanessa Lê, Arthur de Liz Sperb


Overcoming the Creative and Technical Challenges
The challenge was both creative and technical: we had to design and build an entire VR experience from the ground up, ensuring it was not only visually compelling but also ran flawlessly on consumer-grade hardware.







Building the Cast with Character Creator
Our character pipeline is built entirely around Reallusion’s Character Creator and iClone. Reallusion was kind enough to provide us with software licenses to support the production, and we immediately got to work building our cast in Character Creator 5.

To populate scenes quickly with a wide variety of background characters, we used the Reallusion Content Store as a starting point, pulling from their extensive library of 3D people scans and clothing. From a single outfit, we utilized Character Creator’s cloth variation tools to seamlessly swap materials, change colors, and add decals, generating a huge variety of looks highly efficiently. For specialized service uniforms, we modeled the garments ourselves to match current real-world specifications, using tools like MetaTailor to ensure a perfect fit on our character models.



For crowd generation, we also used 3D animations and crowd characters from the ActorCore Asset Store. With over 5,000 motions and 1,000 scanned humans, we selected a variety of crowd characters and significantly accelerated our production. Thanks to the intuitive AI Deep Search, which supports keywords, natural language, and even image-based queries, we were able to find what we needed quickly. The low-polygon 3D people are fully rigged for facial, body, and finger animation, and include complete color variations, enabling efficient crowd generation.


Automating Logic & AI-Assisted Environments
With iClone 8.7, we utilized the new Motion Planning Plugin to automate character logic. Its node-based graph editor allowed us to create intelligent agents that navigate our scenarios autonomously, making our simulations significantly more realistic and faster to build. Simultaneously, the environment team focused on capturing each city’s unique identity. We used a hybrid approach, combining traditional asset libraries with AI-assisted 3D tools. We experimented with platforms like Hunyuan and Tripo, which allowed us to generate buildings and landscape elements directly from image prompts. This helped us create localized architecture that felt specific to each distinct location.


“With iClone 8.7’s Motion Planning, we automated character logic for our VR project — autonomous, reproducible, fast to build. The same workflow is also useful as a stable motion base for AI video-to-video, keeping multiple camera perspectives coherent in complex scenes.”
Marc-André Müller – CoFounder / Technical Director at relative.berlin
Motion Capture and Dialogue Generation
For character motion, we started with in-house recordings and filmed our own team performing the specific actions we needed. Then, we used iClone Video Mocap to turn that raw footage into usable motion capture data. From there, we brought everything into iClone for retargeting and cleanup. We made quick corrections to fine details, such as shoulder alignment, foot contact, and hand placement.


We iterated until the performance was completely ready for the scene. For dialogue, we generated the voice lines with ElevenLabs. We imported the audio directly into iClone and used AccuLIPS to automatically generate lip-sync data. We then did a fast polish pass where needed, which ultimately saved us a massive amount of time in animation.


Unreal Engine Integration and Optimization
Finally, everything came together in Unreal Engine, where we staged and timed our animated characters directly in Sequencer. To keep the VR scenes running smoothly, we focused heavily on performance early in the character pipeline. Before exporting from Character Creator, we used its built-in InstaLOD integration to generate multiple Levels of Detail (LOD) for each character. This crucial step helped us reduce render costs and manage draw calls in complex shots, ensuring the experience stays highly responsive in VR.

Conclusion
Having a consistent pipeline across the whole production was what kept things moving. It helped us stay organized, iterate quickly, and keep the quality steady from our early tests all the way to the final scenes. That structural foundation meant we could put our energy where it belongs: into the creative aspect of the work. Specifically, by shaping performances, building the world, and refining the user experience instead of getting slowed down by technical overhead, we were able to bring the XR Security Lab to life.
This project is funded by the Federal Ministry of Research, Technology, and Space in Germany
Framework programme of the Federal Government 2024 – 2029 Security is of fundamental importance for freedom, quality of life, and prosperity. With its framework programme “Research for civil security 2024-2029”, the Federal Ministry of Education and Research is investing in tomorrow’s security, and working to ensure that people in Germany enjoy the best protection possible – both in everyday life and in disaster situations. Civil security research is key to improving security in all spheres of life in society without disproportionately limiting people’s freedom.

FAQs
How does Character Creator speed up this project?
Character Creator and the Reallusion Content Store played a pivotal role during the process. Character Creator’s cloth variation tools blend well with MetaTailor tools. This workflow blends seamlessly to swap materials, change colors, and add decals on the customized outfit, giving us high efficiency in character customization.
Why is Video Mocap being considered for the animation?
The team has explored both motion-captured suits and AI mocap solutions. After testing, we have found out that we can actually depend on the iClone Video Mocap plugin to get the initial motion, and do some minor motion editing in iClone to get the final animation.
How quickly can we customize a background character?
You can quickly grab a content pack from the Reallusion Content Store and start editing the character from there. For the clothing, in Character Creator, there is a quick way to do it with the MetaTailor Plugin.
Related Posts












































































































































