Title: WristWorld: Generating Wrist-Views via 4D World Models for Robotic Manipulation
Primary area: applications to robotics, autonomy, planning
Keywords: World Model, Embodied AI

Abstract: Wrist-view observations are crucial for VLA models as they capture fine-grained hand–object interactions that directly enhance manipulation performance.
Yet large-scale datasets rarely include such recordings, resulting in a substantial gap between abundant anchor views and scarce wrist views. Existing world models cannot bridge this gap, as they require a wrist-view first frame and thus fail to generate wrist-view videos from anchor views alone.
Amid this gap, recent visual geometry models such as VGGT emerge with precisely the geometric and cross-view priors that make it possible to address such extreme viewpoint shifts.
Inspired by these insights, we propose WristWorld, the first 4D world model that generates wrist-view videos solely from anchor views.
WristWorld operates in two stages: (i) Reconstruction, which extends VGGT and incorporates our proposed Spatial Projection Consistency (SPC) Loss to estimate geometrically consistent wrist-view poses and 4D point clouds; (ii) Generation, which employs our designed video generation model to synthesize temporally coherent wrist-view videos from the reconstructed perspective.
Experiments on Droid, Calvin, and Franka Panda demonstrate state-of-the-art video generation with superior spatial consistency, while also improving VLA performance, raising the average task completion length on Calvin by 3.81% and closing 42.4% of the anchor-wrist view gap. See video results at anonymous page: https://wrist-world.github.io/