Wanjia Fu

Can a world model 🌏 learn to predict how 198 different deformable objects move -- not just one rope or cloth? That question motivated Deform360: 1,980 real-world interactions captured from 41 synchronized views with bimanual touch sensors, which becomes crucial when vision is occluded. Accepted at #ECCV2026. 🧵 1/5

#ECCV2026 Deform360 🌐 Project: deform360.lhy.xyz 📜 arXiv: arxiv.org/abs/2607.05390 💻 Code: github.com/lhy0807/deform… 🤗 Dataset: huggingface.co/datasets/brown…

@zzigakovacic @drsrinathsridha @Hongyu_Lii @CVPR Thank uuu Ziga!!!🤩🤩🤩

@drsrinathsridha @FuWanjia @Hongyu_Lii @CVPR Congrats @FuWanjia !

Super proud of my students and collaborators, especially @FuWanjia and @Hongyu_Lii for the Outstanding Paper Award at the Sense of Space workshop @CVPR for UniTac: ivl.cs.brown.edu/research/unitac

How well can a model watch a short video of some physical dynamics and actually predict what happens next? Introducing MPMWorlds: a new dataset and benchmark to evaluate how well models can reconstruct and extrapolate physical dynamics from video. zzigak.github.io/mpmworlds/ 🧵👇 (1/n)

We interact with dogs through touch -- a simple pat can communicate trust or instruction. Shouldn't interacting with robot dogs be as intuitive? Most commercial robots lack tactile skins. We present UniTac: a method to sense touch using only existing joint sensors! [1/5]