YOSE: 你只选择本质的标记以实现高效的DiT基于视频对象移除
YOSE: You Only Select Essential Tokens for Efficient DiT-based Video Object Removal
AI总结：
YOSE通过引入BVI和DiffSim模块，实现了高效的DiT视频对象移除，通过动态选择关键标记和模拟扩散过程，使推理时间与遮挡区域线性相关，从而在70%的情况下达到2.5倍的加速，同时保持视觉质量。
摘要：
最近的扩散变换器（DiT）基于视频生成技术在视频对象移除中展示了令人印象深刻的结果。然而，这些方法仍然受到显著的推理延迟影响。例如，尽管MiniMax Remover实现了最先进的视觉质量，但其仅以约10帧每秒的速度运行，主要原因是密集计算整个时空标记空间，即使只有小部分遮挡区域需要处理。在本文中，我们提出了YOSE，即
英文摘要：
Recent advances in Diffusion Transformer (DiT)-based video generation technologies have shown impressive results for video object removal. However, these methods still suffer from substantial inference latency. For instance, although MiniMax Remover achieves state-of-the-art visual quality, it operates at only around 10FPS, primarily due to dense computations over the entire spatiotemporal token space, even when only a small masked region actually requires processing. In this paper, we present YOSE, You Only Select Essential Tokens, an efficient fine-tuning framework. YOSE introduces two key components: Batch Variable-length Indexing (BVI) and Diffusion Process Simulator (DiffSim) Module. BVI is a differentiable dynamic indexing operator that adaptively selects essential tokens based on mask information, enabling variable-length token processing across samples. DiffSim provides a diffusion process approximation mechanism for unmasked tokens, which simulates the influence of unmasked regions within DiT self-attention to maintain semantic consistency for masked tokens. With these designs, YOSE achieves mask-aware acceleration, where the inference time scales approximately linearly with the masked regions, in contrast to full-token diffusion methods whose computation remains constant regardless of the mask size. Extensive experiments demonstrate that YOSE achieves up to 2.5X speedup in 70% of cases while maintaining visual quality comparable to the baseline. Code is available at: https://github.com/Wucy0519/YOSE-CVPR26.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
