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ARGUS: Stacked Multi-View Identity Mosaic Injection for Subject-Preserving Video Generation

NeurIPS 2026 · Video › Video generation · approach: Trajectory-guided motion control · 0 citations (OpenAlex)

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Proposes ARGUS, a Wan-2.1-based subject-preserving video generator that injects MLLM-selected image/video identity evidence as a 3x3 stacked mosaic in negative-time, read-only memory, trained without cross-pair data.

Problem

Identity-preserving video generation relies on one or a few static reference images, leading to look-alike fidelity, drift under large yaw/occlusion/scale changes, and reference leakage or brittle cross-pair supervision.

Approach

Represent identity as a diffusion-synchronized 3x3 mosaic of image/micro-clip identity evidence, placed at negative temporal positions in Wan's native token space as read-only memory, with counterfactual nuisance augmentation instead of cross-pair data, plus time-annealed attention and an adaptive learned-skip guidance (ASLG).

Key results

Surprising findings

Limitations

Experimental setup

Datasets: OpenS2V-Eval

Benchmarks: OpenS2V-Eval Human-Domain HardID-Celeb

Metrics: Total Score FaceSim NexusScore NaturalScore GmeScore Aesthetics Motion CLIPScore YawScore OccScore

Baselines: Vidu-2.0 Pika-2.1 Jimeng Hailuo ConsisID EchoVideo SkyReels-A2 HunyuanCustom MAGREF VACE-14B Phantom-14B Stand-In

Builds on: Wan 2.1 flow matching perturbed-attention guidance classifier-free guidance RoPE

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