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
geometric and cross-view priors that make it possible to address 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 Spatial Projection Consistency (SPC) Loss to estimate
geometrically consistent wrist-view poses and 4D point clouds; (ii) Generation,
which employs our 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.