Lookahead Anchoring: Preserving Character Identity in Audio-Driven Human
  Animation
Authors: name: Junyoung Seo, name: Rodrigo Mira, name: Alexandros Haliassos, name: Stella Bounareli, name: Honglie Chen, name: Linh Tran, name: Seungryong Kim, name: Zoe Landgraf, name: Jie Shen

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.