WristWorld: Generating Wrist-Views via 4D World Models for Robotic
  Manipulation
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.
68e71cac7ae125f9582e699d
Zezhong Qian
68e71cac7ae125f9582e699e
Xiaowei Chi
68e71cac7ae125f9582e699f
Yuming Li
68e71cac7ae125f9582e69a0
Shizun Wang
68e71cac7ae125f9582e69a1
Zhiyuan Qin
68e71cac7ae125f9582e69a2
Xiaozhu Ju
68e71cac7ae125f9582e69a3
Sirui Han
68e71cac7ae125f9582e69a4
Shanghang Zhang
WristWorld: Generating Wrist-Views via 4D World Models for Robotic
  Manipulation
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.
WristWorld is a 4D world model that generates wrist-view videos from anchor views, improving video generation consistency and VLA performance.
VGGT Spatial Projection Consistency (SPC) Loss 4D world model wrist-view videos anchor views geometrically consistent wrist-view poses 4D point clouds temporally coherent wrist-view videos Droid Calvin Franka Panda task completion length anchor-wrist view gap