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XGenAct: Geometry-Enhanced World Action Models through Cross-Task Generation

Paper recorded by Signals 4 on 2026-10-02 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-02 on arXiv · recorded by Signals 4 on 2026-10-05

Category: cs.CV · 计算机视觉 · first seen 2026-10-05

Abstract

World action models (WAMs) have advanced robot control by predicting how observations and actions evolve over time. Despite this progress, RGB and action based future prediction does not explicitly address the spatial understanding needed for robot manipulation. Existing efforts often add a limited set of spatial prediction tasks through specialized heads or branches, leaving both the range of spa

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#374 most recent of 756 cs.CV papers we have recorded · ↑ newer: Feedforward Novel View Synthesis for Heterogeneous Cameras · ↓ older: ProgressNet: Sketching and Prompting with a Frozen Text-to-Image Model
Cite this page: XGenAct: Geometry-Enhanced World Action Models through Cross-Task Generation: the #374 most recent of 756 cs.CV papers we have recorded (as of 2026-10-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/xgenact-geometry-enhanced-world-action-models-through-cross-task-generation.html
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