LIVE: Long-horizon Interactive Video World Modeling
Autoregressive video world models predict future visual observations conditioned on actions. While effective over short horizons, these models often struggle with long-horizon generation, as small prediction errors accumulate over time. Prior methods alleviate this by introducing pre-trained teacher models and sequence-level distribution matching, which incur additional computational cost and fail to prevent error propagation beyond the training horizon. In this work, we propose LIVE, a Long-horizon Interactive Video world modEl that enforces bounded error accumulation via a novel cycle-consistency objective, thereby eliminating the need for teacher-based distillation. Specifically, LIVE first performs a forward rollout from ground-truth frames and then applies a reverse generation process to reconstruct the initial state. The diffusion loss is subsequently computed on the reconstructed terminal state, providing an explicit constraint on long-horizon error propagation. Moreover, we provide an unified view that encompasses different approaches and introduce progressive training curriculum to stabilize training. Experiments demonstrate that LIVE achieves state-of-the-art performance on long-horizon benchmarks, generating stable, high-quality videos far beyond training rollout lengths.
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Junchao Huang
claimed_verified
2026-02-04T12:28:01.292Z
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junchao-cuhk
junchao-cuhk
user
junchao-cuhk
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Ziyang Ye
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Xinting Hu
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Tianyu He
claimed_verified
2026-02-06T18:56:14.561Z
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Tianyu He
deeptimhe
user
deeptimhe
6982cb3f9084cb4f0ecb5793
Guiyu Zhang
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Shaoshuai Shi
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Jiang Bian
6982cb3f9084cb4f0ecb5796
Li Jiang
LIVE: Long-horizon Interactive Video World Modeling
Autoregressive video world models predict future visual observations conditioned on actions. While effective over short horizons, these models often struggle with long-horizon generation, as small prediction errors accumulate over time. Prior methods alleviate this by introducing pre-trained teacher models and sequence-level distribution matching, which incur additional computational cost and fail to prevent error propagation beyond the training horizon. In this work, we propose LIVE, a Long-horizon Interactive Video world modEl that enforces bounded error accumulation via a novel cycle-consistency objective, thereby eliminating the need for teacher-based distillation. Specifically, LIVE first performs a forward rollout from ground-truth frames and then applies a reverse generation process to reconstruct the initial state. The diffusion loss is subsequently computed on the reconstructed terminal state, providing an explicit constraint on long-horizon error propagation. Moreover, we provide an unified view that encompasses different approaches and introduce progressive training curriculum to stabilize training. Experiments demonstrate that LIVE achieves state-of-the-art performance on long-horizon benchmarks, generating stable, high-quality videos far beyond training rollout lengths.
LIVE is a long-horizon video world model that uses cycle-consistency and diffusion loss to control error accumulation during extended video generation.
video world models autoregressive models error accumulation cycle-consistency objective diffusion loss forward rollout reverse generation progressive training curriculum