TokenDial: Continuous Attribute Control in Text-to-Video via Spatiotemporal Token Offsets
We present TokenDial, a framework for continuous, slider-style attribute control in pretrained text-to-video generation models. While modern generators produce strong holistic videos, they offer limited control over how much an attribute changes (e.g., effect intensity or motion magnitude) without drifting identity, background, or temporal coherence. TokenDial is built on the observation: additive offsets in the intermediate spatiotemporal visual patch-token space form a semantic control direction, where adjusting the offset magnitude yields coherent, predictable edits for both appearance and motion dynamics. We learn attribute-specific token offsets without retraining the backbone, using pretrained understanding signals: semantic direction matching for appearance and motion-magnitude scaling for motion. We demonstrate TokenDial's effectiveness on diverse attributes and prompts, achieving stronger controllability and higher-quality edits than state-of-the-art baselines, supported by extensive quantitative evaluation and human studies.
69cc052a3f84a0971be66982
Zhixuan Liu
claimed_verified
2026-04-01T08:35:37.870Z
63320a65e092098b57c0917a
/avatars/65b44848b429ddaff5843a8be58f2f0f.svg
Zhixuan Liu
zhixuan2
user
zhixuan2
69cc052a3f84a0971be66983
Peter Schaldenbrand
69cc052a3f84a0971be66984
Yijun Li
69cc052a3f84a0971be66985
Long Mai
69cc052a3f84a0971be66986
Aniruddha Mahapatra
69cc052a3f84a0971be66987
Cusuh Ham
69cc052a3f84a0971be66988
Jean Oh
69cc052a3f84a0971be66989
Jui-Hsien Wang
TokenDial: Continuous Attribute Control in Text-to-Video via Spatiotemporal Token Offsets
We present TokenDial, a framework for continuous, slider-style attribute control in pretrained text-to-video generation models. While modern generators produce strong holistic videos, they offer limited control over how much an attribute changes (e.g., effect intensity or motion magnitude) without drifting identity, background, or temporal coherence. TokenDial is built on the observation: additive offsets in the intermediate spatiotemporal visual patch-token space form a semantic control direction, where adjusting the offset magnitude yields coherent, predictable edits for both appearance and motion dynamics. We learn attribute-specific token offsets without retraining the backbone, using pretrained understanding signals: semantic direction matching for appearance and motion-magnitude scaling for motion. We demonstrate TokenDial's effectiveness on diverse attributes and prompts, achieving stronger controllability and higher-quality edits than state-of-the-art baselines, supported by extensive quantitative evaluation and human studies.
TokenDial enables precise attribute control in text-to-video models by using additive offsets in spatiotemporal token space for coherent edits without retraining.
text-to-video generation spatiotemporal visual patch-token space additive offsets semantic control direction attribute-specific token offsets semantic direction matching motion-magnitude scaling