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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.05871 |
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| _version_ | 1866914380508037120 |
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| author | Xiang, Xunzhi Duan, Zixuan Zhang, Guiyu Zhang, Haiyu Gao, Zhe Wu, Junta Zhang, Shaofeng Wang, Tengfei Fan, Qi Guo, Chunchao |
| author_facet | Xiang, Xunzhi Duan, Zixuan Zhang, Guiyu Zhang, Haiyu Gao, Zhe Wu, Junta Zhang, Shaofeng Wang, Tengfei Fan, Qi Guo, Chunchao |
| contents | Distilled autoregressive diffusion models facilitate real-time short video synthesis but suffer from severe error accumulation during long-sequence generation. While existing Test-Time Optimization (TTO) methods prove effective for images or short clips, we identify that they fail to mitigate drift in extended sequences due to unstable reward landscapes and the hypersensitivity of distilled parameters. To overcome these limitations, we introduce Test-Time Correction (TTC), a training-free alternative. Specifically, TTC utilizes the initial frame as a stable reference anchor to calibrate intermediate stochastic states along the sampling trajectory. Extensive experiments demonstrate that our method seamlessly integrates with various distilled models, extending generation lengths with negligible overhead while matching the quality of resource-intensive training-based methods on 30-second benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_05871 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Pathwise Test-Time Correction for Autoregressive Long Video Generation Xiang, Xunzhi Duan, Zixuan Zhang, Guiyu Zhang, Haiyu Gao, Zhe Wu, Junta Zhang, Shaofeng Wang, Tengfei Fan, Qi Guo, Chunchao Computer Vision and Pattern Recognition Distilled autoregressive diffusion models facilitate real-time short video synthesis but suffer from severe error accumulation during long-sequence generation. While existing Test-Time Optimization (TTO) methods prove effective for images or short clips, we identify that they fail to mitigate drift in extended sequences due to unstable reward landscapes and the hypersensitivity of distilled parameters. To overcome these limitations, we introduce Test-Time Correction (TTC), a training-free alternative. Specifically, TTC utilizes the initial frame as a stable reference anchor to calibrate intermediate stochastic states along the sampling trajectory. Extensive experiments demonstrate that our method seamlessly integrates with various distilled models, extending generation lengths with negligible overhead while matching the quality of resource-intensive training-based methods on 30-second benchmarks. |
| title | Pathwise Test-Time Correction for Autoregressive Long Video Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.05871 |