WildActor: Unconstrained Identity-Preserving Video Generation
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.
69ae7f539893f58272f741f3
Qin Guo
claimed_verified
2026-03-09T08:35:08.242Z
658e7e4d9e16fa751005bf7a
/avatars/8deb66b73a37f49ad7e33d5ebc3432aa.svg
guoqin
guoqin
user
guoqin
69ae7f539893f58272f741f4
Tianyu Yang
69ae7f539893f58272f741f5
Xuanhua He
69ae7f539893f58272f741f6
Fei Shen
69ae7f539893f58272f741f7
Yong Zhang
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Zhuoliang Kang
69ae7f539893f58272f741f9
Xiaoming Wei
69ae7f539893f58272f741fa
Dan Xu
claimed_verified
2026-03-09T09:39:35.343Z
66feab48651e00e22f33222e
/avatars/7344377e2c796c7ec85194bb2fc78521.svg
Dan Xu
danxuhk
user
danxuhk
WildActor: Unconstrained Identity-Preserving Video Generation
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.
WildActor generates consistent human videos with full-body identity preservation across varying viewpoints and motions using a large-scale dataset and novel attention mechanisms.
human video generation identity consistency viewpoint adaptation asymmetric identity-preserving attention viewpoint-adaptive monte carlo sampling manifold coverage body identity preservation