Video Compression Meets Video Generation: Latent Inter-Frame Pruning with Attention Recovery
Current video generation models suffer from high computational latency, making real-time applications prohibitively costly. In this paper, we address this limitation by exploiting the temporal redundancy inherent in video latent patches. To this end, we propose the Latent Inter-frame Pruning with Attention Recovery (LIPAR) framework, which detects and skips recomputing duplicated latent patches. Additionally, we introduce a novel Attention Recovery mechanism that approximates the attention values of pruned tokens, thereby removing visual artifacts arising from naively applying the pruning method. Empirically, our method increases video editing throughput by $1.53\times$, achieving an aver...
| Authors | Dennis Menn, Yuedong Yang, Bokun Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 6 March 2026 |
| Last updated at source | 28 April 2026 |
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