People are wired to think visually — a table of raw data doesn’t communicate on its own, no matter how important the underlying research is.
Orbital Materials was raising funding for their AI system that designs new materials, and needed visually convincing animations of the underlying process to help make that case to investors. That’s a specific case of a broader problem.
Someone has to translate raw data into something people respond to, and the industry has two options, both flawed in opposite directions.
Traditional data visualization
Accurate, but built for scientists, not for anyone else — dry, technical, and easy to tune out.
Traditional motion graphics
Visually engaging, but disconnected from real data. Hand-animated to look a certain way, not driven by an actual dataset — so however good they look, they can’t represent real research at all.
For a company trying to raise funding, that gap has direct consequences: convincing investors depends on communicating a genuinely technical process in a way non-specialists find compelling, and neither existing approach does that alone.
Rather than producing a single animation for one dataset, I built a system that could take in any dataset from Orbital’s process and repeatably produce accurate, visually engaging output from it — without rebuilding the process from scratch each time.
01 · Import dataset — the AI denoising data coming directly from Orbital’s system
02 · Import periodic table — reference so each element gets a consistent, physically-accurate size and colour
03 · Match databases — cross-reference dataset elements against the periodic table, so e.g. hydrogen renders at correct relative size and colour every time
04 · Remap timestep to frame — maps the dataset’s per-particle frame number directly onto Houdini’s timeline, so playback is accurate without manual timing
05 · Create intersecting lines — optionally visualizes connections between elements where relevant to the story
06 · Create shaders — shades each element based on its matched colour data
07 · Add background, create lights, render
Funded
The resulting animations were used as part of Orbital’s investor materials for a funding round that closed successfully
Repeatable
A repeatable system for visualizing Orbital’s AI denoising process — any new dataset from their pipeline could be run through it without rebuilding the process by hand
The core idea — importing a real dataset and converting it directly into geometry, rather than hand-animating an approximation of it — isn’t specific to materials science. Any process that produces structured, timestamped data can be run through the same system. It also extends naturally beyond rendering: the same converted geometry could feed into simulation inputs or particle systems, not just visual output.
Challenges and future direction
Manual lighting & environments
Backgrounds and lighting are currently set manually per project. Automating that — generating appropriate environments and lighting conditions based on the data or context — is a clear next step.
Manual title/text layer
The final deliverable was a video with animated text and titles layered on top of the rendered data. That layer is currently manual too, but doesn’t need to be — using Cavalry, title and text animation could be driven directly from a Google Sheet or another dataset, closing the gap between raw data and finished video entirely.