前瞻锚定：在音频驱动人体动画中保持角色身份一致性

摘要

音频驱动的人体动画模型在时序自回归生成过程中常出现身份漂移问题，即角色随时间推移逐渐丧失身份特征。现有解决方案通过生成关键帧作为中间时序锚点来防止质量退化，但这需要额外增加关键帧生成阶段，且可能限制自然运动动态。为此，我们提出前瞻锚定技术，其核心在于利用当前生成窗口前方未来时间步的关键帧，而非窗口内部的关键帧。这种方法将关键帧从固定边界转换为方向性航标：模型在响应即时音频线索的同时持续追踪这些未来锚点，通过持久化引导保持身份一致性。该技术还可实现自关键帧生成，即将参考图像直接作为前瞻目标，完全省去关键帧生成步骤。我们发现前瞻时间距离能自然控制表现力与一致性之间的平衡：较大距离允许更大幅度的运动自由度，较小距离则强化身份特征保持。在三个最新人体动画模型上的实验表明，前瞻锚定技术实现了更优的唇形同步度、身份保持度和视觉质量，在不同架构上均展现出改进的时序条件控制效果。视频结果请访问：https://lookahead-anchoring.github.io。

English

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.

前瞻锚定：在音频驱动人体动画中保持角色身份一致性

Lookahead Anchoring: Preserving Character Identity in Audio-Driven Human Animation

摘要

Support