Co-founder & CEO @getcartwheel. Previously, Google. Also Universal Patterns. Love animation, diagrams & explainers.
Joined March 2009
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Retargeting is finally getting its due in the academy!
yy-gx.github.io/GNR/
Jonathan Jarvis retweeted
if you want to make it move, you know where to go (policy training)
cc @ClementDelangue 🤗🤸
Welcome to @huggingface family @asimovinc !
Very excited about this collaboration @menloresearch !
Sound on ! 🎶
Soft objects are hard to make in simulation.
Impressive work from the @Meta team!
projectsuperdex.com/
Jonathan Jarvis retweeted
Spent a bunch of tokens over the last few weeks building a fully procedural New York simulation.
mrdoob.github.io/slop-newyor…
It's only 784KB gzipped and the code is on github, so you can do whatever you want with it ✌️
We make decisions about video A LOT. Excited for these video structured decision models.
VideoJev
System One Model for Video Understanding and Interaction
paper: huggingface.co/papers/2610.1…
Jonathan Jarvis retweeted
VideoJev
System One Model for Video Understanding and Interaction
paper: huggingface.co/papers/2610.1…
The leap in realism is what gets me.
Meet Swing Choreo, our new generative motion model at @getcartwheel .
Up to four people performing together, with nuanced interactions. Longer sequences of actions, much more faithful to your prompt. Expressive faces, hands with great finger motion, people of different sizes moving across ground at different heights.
For studios, this is the jump from individual actions for individual characters to whole scenes, with timing and interactions generated together.
For robotics teams, it opens up hours of varied, realistic human motion for simulation.
And with agents like Astra and Opus, you can generate entire, stylistically consistent motion libraries for multiple characters at a time.
A huge step forward. Very proud of this team.
#Astra #Opus #Claude #chatGPT #Robotics
The next generation of explainer graphics will be incredible
I asked Claude Opus 5.5 to explain how a rocket engine works by building an interactive Raptor 3 you can take apart in your browser.
Cut it open, follow the oxygen and the methane through both turbopumps, then throttle it and watch the shock diamonds move.
airsup.ai/rocket-engine
A tennis swing is a person AND a racket. Now we can capture both in 3D from ordinary video.
We’ve been working toward this for a long time at @getcartwheel. Still early, but I’m so proud of Andrew and this team.
What would you want to capture first?
Ah!! We've been trying to get this working for months.
Basically, we can now capture a person and the objects they're interacting with in 3D from regular video. Motion for both, plus approximate object shape.
I've been excited about this for a while because so much of what people do involves stuff. If you want a robot to learn how to use a tool, or to understand someone's tennis swing, you probably want to know what the tool or racket is doing too.
Same goes for props in animation and everyday tasks in occupational therapy. There's a lot here.
Still imperfect, but this week it started working well enough that I wanted to show you.
Introducing compute-optimal scaling laws for human motion generation!
Human motion generation has historically been niche and difficult to scale. Animators, roboticists, motion analysts, and game developers all see the potential, but it rarely lives up to the rigor required in production.
We have long believed this is because all prior systems have been fantastically data constrained. Until today, we haven't been able to prove that scaling would actually help.
Today we present our flagship research paper: Compute-Optimal Scaling Laws for Human Motion Generation. (link: github.com/Cartwhl/scaling-l…)
We are building the world's largest, high-quality human-motion dataset. We trained hundreds of models across varying scales. They match the Chinchilla scaling found in language.
This places motion as the fifth data modality we understand how to scale. Text, images, audio, and video all have scaling laws. None of them really reflect the physical world. 3D human motion, across time, does.
Both autoregressive and flow matching motion models scale predictably. We can now predict, before training a model, how good it will be.
Motion is no longer data constrained. It is now a predictable and scalable frontier for physical and digital intelligence. Our largest models are training now, and the gap between generated motion and the real physical world is closing fast.
Paper: github.com/Cartwhl/scaling-l…
Blog: getcartwheel.com/blog/scalin…
Gemini 3.7 Flash is excellent at agentic vision tasks. We've been working with it for some time now. It's fast. And cheap. @andrew_n_carr likes it! 🤸♀️
#Google #Gemini
Less than two weeks since our last update, and we've launched a new version of our perception model, Comic 4.3.
It's much much faster — now the fastest pro quality motion capture!
Best of all, it's just as fast for multiple characters as for a single character. And, you can batch process all your shots at the same time. So, 1 or 100 performances — super fast 🤸♂️
Also cool, you can use it directly in Unreal Engine, and soon Maya, Blender and Unity.
Live @getcartwheel.
We ran some shots through different tools and timed it all to see the speed.
#robotics #unreal #maya #blender #AI #unity #mocap #computervision