ARGUS: Stacked Multi-View Identity Mosaic Injection for Subject-Preserving Video Generation
Abstract
Identity-preserving video generation is fundamentally constrained by the prevailing point-reference paradigm: existing methods consume one or a few snapshot images and therefore cannot transcend "look-alike" fidelity, suffer from severe drift under large yaw, occlusion or scale variation, and trade brittle cross-pair supervision against pervasive reference leakage. We argue that identity is intrinsically a spatially-distributed, multi-view concept and propose Argus, a theoretically-grounded framework that lifts identity injection from single-snapshot conditioning to multi-view aggregated reasoning. Argus comprises three tightly-coupled modules. (i) An Identity Director built on a frozen multimodal LLM automatically curates nine semantically-diverse keyframes, extracts a global identity descriptor, and rewrites the prompt to dissolve the prompt-frame-identity tri-conflict. (ii) A Stacked Mosaic Identity Injection packs the nine SMPL-localized crops into a 3×3 mosaic and feeds it through Wan 2.1's native 36-channel patch embedding via a flow-matching-synchronized prefix-token stream, while a Negative-Time-Anchored RoPE preserves architectural compatibility with Wan's split-axis design. (iii) An Adaptive Skip-Layer Guidance replaces hand-tuned PAG with a tiny mask network that learns where to perturb, decoupling identity guidance from textual guidance, and a Temporal Identity Annealing schedule injects identity only during the structure-formation window to obviate copy-paste artifacts. Trained without any cross-pair data, Argus attains state-of-the-art performance on OpenS2V-Eval, surpassing both closed-source (Hailuo) and open-source (VACE-14B, Phantom-14B, Stand-In, BindWeave) systems on five of seven metrics. To stress-test extreme failure modes, we additionally release HardID-Celeb, a celebrity benchmark with two new robustness probes (YawScore, OccScore) on which Argus establishes the highest scores by a wide margin. A 47-participant double-blind user study confirms perceptual superiority across both expert and lay populations.