Month: May 2026

This week did not have a major workshop or technical lecture. Instead, Serra focused on explaining the requirements for the final submission and showed examples of how students from previous years had structured their work. I found this really useful because I had been feeling quite overwhelmed by the number of deliverables and was still unclear about what was actually expected from us. Seeing examples of successful submissions helped me understand how everything could be presented and gave me a clearer picture of how to organise our own documentation.

The rest of the session was dedicated to project work. During a brief discussion with Serra, she once again stressed the importance of clarity throughout the project. Although our concept involving AI and prediction algorithms was technically interesting, she reminded us that the audience needed to understand what they were seeing and why it mattered. One suggestion she made was to film the interaction inside the 270-degree screen room. She explained that even a short clip showing someone entering the installation and interacting with it would communicate the purpose of the project far better than screenshots alone. This advice made us think more carefully about how the final work would be experienced and documented.

We spent the remainder of the session working on the next stage of the project, which involved generating heat maps and motion trails from the movement data we had collected. After searching through tutorials and experimenting with different approaches, we were able to produce some visuals that we were genuinely excited about. Seeing these first visualisations emerge from the raw data was encouraging, as it felt like the project was finally beginning to take shape beyond the technical setup and into something with a stronger visual identity.

This week we explored Reality Capture in Unreal Engine using Reality Scan. We began the session by selecting an object to record, and Serra gave us advice on how to achieve the best results. She stressed the importance of capturing footage slowly, ensuring all angles were covered and avoiding reflective or shiny objects, as these tend to confuse the software. I decided to scan my headphones, and after processing the footage we imported the model into Unreal, cleaned up the mesh and applied textures. Serra also demonstrated how the recorded object could be imported as a point cloud, which I found particularly interesting. Seeing the object represented as thousands of points immediately made me think about the possibility of animating point cloud values to create abstract and dynamic visuals.

Although I enjoyed the process, the workshop confirmed that photogrammetry is probably not something I would pursue extensively. I enjoy modelling assets from scratch, and I found the process of cleaning and correcting scans to be time-consuming and often inaccurate. While the technology is impressive, I personally prefer the control and precision that traditional modelling offers. However, I could still see potential for using point clouds and imperfect scans creatively rather than striving for realism.

Outside of the workshop, my group began developing the technical side of our project. Having previously experimented with MediaPipe during a TouchDesigner workshop, I spent some time brushing up on it before introducing it to the rest of the team. Once we were able to successfully track body motion, we started discussing which parameters would form the basis of our prediction algorithms and visualisations. We eventually settled on tracking hip, wrist and shoulder positions. By feeding this data into the system, we were able to generate a line graph representing changes in movement over time. Reaching this stage felt like an important milestone, as it marked the completion of the first technical stage of the project and gave us a foundation to build more complex visualisations from.

This week we had a short but enjoyable workshop exploring 360° video production in Unreal Engine using Off World Live. Using environments downloaded from Fab, we learned how to render equirectangular 360° videos directly from Unreal before encoding the output in Adobe Media Encoder. Although the process itself was relatively straightforward, it was interesting to see how quickly immersive content could be produced using real-time workflows. The session also gave me a better understanding of the technical pipeline involved in creating 360° experiences, which I found particularly relevant given my interest in immersive storytelling.

After becoming familiar with the workflow, I spent some time experimenting with different scenes and camera positions. Seeing the results in a 360° format highlighted how different spatial composition becomes when there is no fixed frame. It also reinforced some of the ideas I have been exploring in my thesis regarding directing attention within immersive environments. Despite being a relatively short workshop, it was one of the more enjoyable sessions due to how quickly we were able to move from setup to final output.

Our group also used the opportunity to further discuss our project. By this stage, we had begun settling on the narrative we wanted to communicate and started dividing the production into different stages. This helped us establish a clearer structure for the project and think about how the different screens and visual elements would interact with one another. Having a more defined narrative allowed us to begin planning the screen layout and think more carefully about how the audience would experience the progression of the video.

This week we received feedback on our group project and discussed both the creative direction and the technical feasibility of our ideas. Since our concept revolves around AI, data extraction and prediction algorithms based on everyday movement, a large part of the conversation focused on whether we would realistically have access to the tools and infrastructure needed to realise the project. We discussed possible tracking methods and interactive technologies, and Serra suggested looking into devices such as Kinect and Leap Motion for capturing motion more accurately than a webcam. This was particularly useful because it encouraged us to think about the practical side of the project before becoming too attached to ideas that might be difficult to implement.

Serra also emphasized the importance of visualization. Rather than simply collecting data, she encouraged us to think about how that data could be translated into something visually engaging and understandable to an audience. She stressed that the visuals themselves should communicate the story and make the relationship between movement, prediction and AI clear. We were advised to research artists working with data vizualisation, glitch aesthetics and experimental digital media. One artist that was recommended was Rosa Menkman, whose work explores digital artifacts, glitches and the ways technology shapes visual communication.

Reflecting on the feedback, I realized that our project needed to be driven as much by visual communication as by technology. It is easy to become fascinated with AI and data collection, but Serra’s comments reminded us that the audience ultimately experiences the visuals rather than the underlying code. Going forward, I want to spend more time studying how artists visualize complex information and how abstract systems can be translated into images and animations that tell a clear story.

This week we explored ND displays in Unreal Engine and discussed how similar technologies are used in large-scale digital billboards and public advertising displays. I found the session particularly interesting because it demonstrated how real-time graphics can be used outside of traditional film and animation contexts. The lecture began with a guided exercise where we followed Serra’s instructions to create a basic display setup and understand the underlying workflow.

Once everyone had completed the initial exercise, we were given the freedom to experiment with our own designs, animations and visual styles. This was my favourite part of the session as it encouraged exploration rather than simply following a tutorial. Being able to modify the display and test different animations helped me understand the system much more quickly than if I had only recreated the example.

I also used part of the session to discuss our project with my group members. By this stage, we were beginning to narrow down our ideas and were particularly interested in themes surrounding AI and data extraction. One concept we discussed was using prediction algorithms to analyse everyday human movement and behaviour, exploring how seemingly ordinary actions can become data that is collected, interpreted and used to predict future outcomes. Although the project was still in its early stages, the conversation helped us identify a direction that connected our interests in emerging technology, real-time media and experimental storytelling.