Pathwise Test-Time Correction for Autoregressive Long Video Generation
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.
69855e014ad556f294b7eb4d
Xunzhi Xiang
69855e014ad556f294b7eb4e
Zixuan Duan
69855e014ad556f294b7eb4f
Guiyu Zhang
69855e014ad556f294b7eb50
Haiyu Zhang
69855e014ad556f294b7eb51
Zhe Gao
69855e014ad556f294b7eb52
Junta Wu
69855e014ad556f294b7eb53
Shaofeng Zhang
69855e014ad556f294b7eb54
Tengfei Wang
69855e014ad556f294b7eb55
Qi Fan
69855e014ad556f294b7eb56
Chunchao Guo
Pathwise Test-Time Correction for Autoregressive Long Video Generation
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.
Test-Time Correction addresses error accumulation in distilled autoregressive diffusion models for long-video synthesis by using initial frames as reference anchors to calibrate stochastic states during sampling.
distilled autoregressive diffusion models Test-Time Optimization Test-Time Correction error accumulation long-sequence generation reward landscapes hypersensitivity stochastic states sampling trajectory