Test-Time Noise Guided Adaptation for Realistic Autoregressive Video Generation

Autoregressive video diffusion models have enabled the gener-ation of arbitrarily long videos by removing conditioning on future frames,thus greatly improving computational efficiency. Yet, they suffer from er-ror accumulation over time, as the denoised sequence gradually drifts awayfrom the conditioning distribution seen during training. Recent advancesattempt to reduce this error by anchoring each generated frame to thelearned manifold of real ones. However, even when all generated individualframes lie close to the real manifold, there are trajectories which the modellacks sufficient knowledge to continue without exiting it, thus reachinga terminal point. To prevent the model from being trapped in terminalpoints, we start from the hypothesis that for well-modeled future trajec-tories the distribution of the predicted noise should match the one of theforward noising process. To enforce such a prior at test time, we introduceTerminal points Avoidance through Noise Guided Optimization (TANGO),which uses the diffusion model as a critic of its own outputs, by predictingone step forward and requiring an isotropic Gaussian noise prediction. Weuse the deviation from this expected noise distribution to search for an al-ternative trajectory that does not lead to a terminal point. Our approachachieves a 3.1% absolute improvement on VBench over state-of-the-art,while reducing Fréchet Video Distance by 28.3% on average across 15 svideos. Our code is available on https://mever-team.github.io/tango.