---
title: "Cycle WAM: Coupling Action and Video through Cycle Consistency"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.04711v1"
permalink: "https://feed.astralor.com/items/itm_08cc67adbf64f277c9459c12"
published: "2026-11-03T11:33:51.000Z"
score: 60
channel: "tech"
tags: ["多模态","Cycle WAM","Coupling Action","Video","Cycle Consistency Recent","WAMs","Existing","Recent"]
---

# Cycle WAM: Coupling Action and Video through Cycle Consistency

## 速览

Recent advances in video modeling have driven substantial progress in world action models (WAMs). Existing approaches exploit pretrained video priors by decoding actions from latent representations or inferring them fro…

## 关键点

- Recent advances in video modeling have driven substantial progress in world action models (WAMs).
- Existing approaches exploit pretrained video priors by decoding actions from latent representations or inferring them from generated videos.
- The underlying link is causal: robot actions produce changes in the scene, and video observations of these changes provide evidence for inferring the actions that produced them.

## 全文

Recent advances in video modeling have driven substantial progress in world action models (WAMs). Existing approaches exploit pretrained video priors by decoding actions from latent representations or inferring them from generated videos. The underlying link is causal: robot actions produce changes in the scene, and video observations of these changes provide evidence for inferring the actions that produced them. However, existing training formulations do not by themselves ensure that actions and their predicted visual consequences are consistent, potentially limiting the use of pretrained capabilities. We treat video and action as paired modalities linked by this causal relationship and learn their correspondence through a unified training framework designed to better exploit pretrained video priors. Cycle WAM first augments video-action co-training with bidirectional conversion to reinforce cross-modal correspondence and recover forward and inverse dynamics. It then introduces a differentiable cycle-reconstruction objective to further couple the two mappings. The resulting model supports both forward and inverse dynamics, with the potential to improve performance through more effective use of pretrained video priors. Cycle WAM achieves state-of-the-art performance among comparable methods on LIBERO-Pro and substantially improves DreamZero on DROID. Comparisons with UWM characterize its inverse-dynamics performance and show sharper visual detail in the displayed forward-dynamics predictions.

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