Lookahead Anchoring: Preserving Character Identity in Audio-Driven Human
  Animation
Audio-driven human animation models often suffer from identity drift during
temporal autoregressive generation, where characters gradually lose their
identity over time. One solution is to generate keyframes as intermediate
temporal anchors that prevent degradation, but this requires an additional
keyframe generation stage and can restrict natural motion dynamics. To address
this, we propose Lookahead Anchoring, which leverages keyframes from future
timesteps ahead of the current generation window, rather than within it. This
transforms keyframes from fixed boundaries into directional beacons: the model
continuously pursues these future anchors while responding to immediate audio
cues, maintaining consistent identity through persistent guidance. This also
enables self-keyframing, where the reference image serves as the lookahead
target, eliminating the need for keyframe generation entirely. We find that the
temporal lookahead distance naturally controls the balance between expressivity
and consistency: larger distances allow for greater motion freedom, while
smaller ones strengthen identity adherence. When applied to three recent human
animation models, Lookahead Anchoring achieves superior lip synchronization,
identity preservation, and visual quality, demonstrating improved temporal
conditioning across several different architectures. Video results are
available at the following link: https://lookahead-anchoring.github.io.
690043b122d452aac6dd43db
Junyoung Seo
claimed_verified
2026-03-17T08:32:28.332Z
635bcc3086f52b24c1e6422d
/avatars/078710e49e849e2d890b4496bdc72d0d.svg
Junyoung Seo
jyseo
user
jyseo
690043b122d452aac6dd43dc
Rodrigo Mira
690043b122d452aac6dd43dd
Alexandros Haliassos
690043b122d452aac6dd43de
Stella Bounareli
690043b122d452aac6dd43df
Honglie Chen
690043b122d452aac6dd43e0
Linh Tran
690043b122d452aac6dd43e1
Seungryong Kim
690043b122d452aac6dd43e2
Zoe Landgraf
690043b122d452aac6dd43e3
Jie Shen
Lookahead Anchoring: Preserving Character Identity in Audio-Driven Human
  Animation
Audio-driven human animation models often suffer from identity drift during
temporal autoregressive generation, where characters gradually lose their
identity over time. One solution is to generate keyframes as intermediate
temporal anchors that prevent degradation, but this requires an additional
keyframe generation stage and can restrict natural motion dynamics. To address
this, we propose Lookahead Anchoring, which leverages keyframes from future
timesteps ahead of the current generation window, rather than within it. This
transforms keyframes from fixed boundaries into directional beacons: the model
continuously pursues these future anchors while responding to immediate audio
cues, maintaining consistent identity through persistent guidance. This also
enables self-keyframing, where the reference image serves as the lookahead
target, eliminating the need for keyframe generation entirely. We find that the
temporal lookahead distance naturally controls the balance between expressivity
and consistency: larger distances allow for greater motion freedom, while
smaller ones strengthen identity adherence. When applied to three recent human
animation models, Lookahead Anchoring achieves superior lip synchronization,
identity preservation, and visual quality, demonstrating improved temporal
conditioning across several different architectures. Video results are
available at the following link: https://lookahead-anchoring.github.io.
Lookahead Anchoring improves audio-driven human animation by using future keyframes as dynamic guides, enhancing lip synchronization, identity preservation, and visual quality.
temporal autoregressive generation identity drift keyframes lookahead anchoring directional beacons self-keyframing temporal lookahead distance temporal conditioning