EverAnimate: Minute-Scale Human Animation via Latent Flow Restoration

We propose **EverAnimate**, an efficient post-training method for long-horizon animated video generation that preserves visual quality and character identity. Long-form animation remains challenging because highly dynamic human motion must be synthesized against relatively static environments, making chunk-based generation prone to accumulated drift: (i) low-level quality drift, such as progressive degradation of static backgrounds, and (ii) high-level semantic drift, such as inconsistent character identity and view-dependent attributes. To address this issue, **EverAnimate** restores drifted flow trajectories by anchoring generation to a persistent latent context memory, consisting of two complementary mechanisms. *(i) Persistent Latent Propagation* maintains a context memory across chunks to propagate identity and motion in latent space while mitigating temporal forgetting. *(ii) Restorative Flow Matching* introduces an implicit restoration objective during sampling through velocity adjustment, improving within-chunk fidelity. With only lightweight LoRA tuning, **EverAnimate** outperforms state-of-the-art long-animation methods in both short- and long-horizon settings: at 10 seconds, it improves PSNR/SSIM by 8%/7% and reduces LPIPS/FID by 22%/11%; at 90 seconds, the gains increase to 15%/15% and 32%/27%, respectively. Anonymous project page: [https://anonymous-xxx-lab.github.io/](https://anonymous-xxx-lab.github.io/).