TANGO

Test-Time Noise Guided Adaptation for Realistic Autoregressive Video Generation

Dimitrios Karageorgiou^1,2 · Symeon Papadopoulos¹ · Ioannis Kompatsiaris¹ · Efstratios Gavves²

¹ Information Technologies Institute, CERTH · ² University of Amsterdam

ECCV 2026

The code has not been released yet. Watch this repository to be notified.

Autoregressive video diffusion models eventually collapse. Prior works aim to keep each frame on the manifold of real ones, but a trajectory whose every frame looks right can still reach a terminal point: a state in the manifold of real videos that the model lacks the knowledge to continue. TANGO detects terminal points at test time from the model's own noise predictions. Then, test-time adaptation is employed to steer the model away from terminal points, effectively trading inference-time compute for improved performance.

Citation

@inproceedings{karageorgiou2026tango,
  title     = {Test-Time Noise Guided Adaptation for Realistic Autoregressive Video Generation},
  author    = {Karageorgiou, Dimitrios and Papadopoulos, Symeon and Kompatsiaris, Ioannis and Gavves, Efstratios},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year      = {2026}
}

Acknowledgments

Supported by the Horizon Europe projects ELIAS (grant no. 101120237) and ELLIOT (grant no. 101214398). Computational resources were granted with the support of GRNET.