Скачать или смотреть This AI Predicts Future Motion Better Than Humans (Secret Method)

🚀 Can a model predict what happens next on a slackline — like a human would? In this video, we dive into **Autoregressive Flow Matching (ARFM)**, a breakthrough in future motion prediction that’s fast, general, and grounded in physics and intent.

🔥 You’ll learn:
How ARFM learns from *millions of unlabeled videos* (YouTube, robotics, more) using *automated point tracking* — no manual labels needed!
Why most motion models fail: they’re narrow, slow, or get physics wrong. ARFM fixes this with *autoregressive reasoning + flow matching* for smooth, realistic, and *uncertainty-aware* predictions.
How it runs *in real time* by predicting sparse point tracks — not full pixels — making it perfect for robotics, AR/VR, and interactive AI.
Real-world results: *10%+ task success boost* on CALVIN robotics benchmark (zero-shot!), and top performance on UCF-101 — even beating models trained on the same data!
How it powers *natural human-object interaction synthesis* — not just motion, but meaningful action.

💡 This isn’t just another motion predictor. It’s a *foundation model for future motion* — scalable, efficient, and open-sourced at [github.com/Johnathan-Xie/arfm-motion-prediction](https://github.com/Johnathan-Xie/arfm-moti....

Perfect for AI/ML beginners and experts alike who want to build smarter, faster, and more intuitive AI systems.

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Read more on arxiv by searching for this paper: 2512.22688v1.pdf