Diffusion forcing is great for sequence modeling!
— Angjoo Kanazawa (@akanazawa) 31 mars 2026
We've been working on social behavior prediction but nothing's worked this well.
Representation matters: We encode motion with discrete latents & model relative relationships in a neat way. It handles multiple tasks & people! https://t.co/HpK0SiJ3iT
Diffusion forcing is great for sequence modeling! We've been working on social behavior prediction but nothing's worked this well. Representation matters: We encode motion with discrete latents & model relative relationships in a neat way. It handles multiple tasks & people! Vongani Maluleke (@vonekels) When people share a space, their movements become intertwined. Embodied agents need to understand these social dynamics to interact effectively. Introducing MAGNet 🧲, a unified autoregressive diffusion forcing model for multi-agent motion generation that captures these interactions. MAGNet is flexible: predict the future, fill in missing motion, or have people react to each other, all while naturally scaling to N>2 people and generating ultra-long motion sequences. — https://nitter.net/vonekels/status/2037350776776061160#m
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