让视频人物全身形象不变，适应任意视角和动作。
WildActor: Unconstrained Identity-Preserving Video Generation
- 用不对称身份保持注意力+视角自适应采样，动态优化参考条件。
- 在1800万张图像、160万段视频上训练，实现跨视角身份一致。
- 适合影视生成、虚拟主播等需稳定人物形象的场景。
高质量数字演员生成要求在动态镜头、多变视角和复杂动作下保持全身形象一致，现有方法常因聚焦面部而忽略身体一致性，或因姿态锁定产生僵硬复制粘贴效果。我们构建了包含1800万张人体图像和160万段视频的Actor-18M大规模数据集，覆盖任意视角与标准三视图。基于此，提出WildActor框架，支持任意视角条件下的真人视频生成。引入非对称身份保持注意力机制与视角自适应蒙特卡洛采样策略，通过边际效用迭代重加权参考条件，实现均衡流形覆盖。在新提出的Actor-Bench评测中，WildActor在多样镜头构图、大视角变化和大幅动作下均显著优于现有方法，持续保持身体身份一致性。
原文摘要 · Abstract (English)
Production-ready human video generation requires digital actors to maintain strictly consistent full-body identities across dynamic shots, viewpoints and motions, a setting that remains challenging for existing methods. Prior methods often suffer from face-centric behavior that neglects body-level consistency, or produce copy-paste artifacts where subjects appear rigid due to pose locking. We present Actor-18M, a large-scale human video dataset designed to capture identity consistency under unconstrained viewpoints and environments. Actor-18M comprises 1.6M videos with 18M corresponding human images, covering both arbitrary views and canonical three-view representations. Leveraging Actor-18M, we propose WildActor, a framework for any-view conditioned human video generation. We introduce an Asymmetric Identity-Preserving Attention mechanism coupled with a Viewpoint-Adaptive Monte Carlo Sampling strategy that iteratively re-weights reference conditions by marginal utility for balanced manifold coverage. Evaluated on the proposed Actor-Bench, WildActor consistently preserves body identity under diverse shot compositions, large viewpoint transitions, and substantial motions, surpassing existing methods in these challenging settings.
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