MiniMax-Remover：驯服不良噪声助力视频目标移除
MiniMax-Remover: Taming Bad Noise Helps Video Object Removal
AI总结：
提出MiniMax-Remover两阶段视频目标移除方法，通过简化模型架构并利用极小极大优化策略驯服对抗性噪声，仅需6步且无需CFG即实现高效高质量移除。
摘要：
视频扩散模型的最新进展推动了视频编辑技术的快速进步。然而，由于幻觉目标和视觉伪影等问题，作为视频编辑关键子任务的视频目标移除仍具挑战性。此外，现有方法通常依赖计算成本高昂的采样过程和无分类器引导（CFG），导致推理缓慢。为了解决这些限制，我们提出了 MiniMax-Remover，一种新颖的两阶段视频目标移除方法。基于文本条件并不最适合该任务的观察，我们在第一阶段通过移除文本输入和交叉注意力层来简化预训练视频生成模型，从而得到更轻量、高效的模型架构。在第二阶段，我们使用极小极大优化策略，在由阶段1模型生成并经人工标注者筛选的成功视频上蒸馏移除器，以进一步提升编辑质量和推理速度。具体而言，内部最大化识别导致移除失败的对抗性输入噪声（“不良噪声”），而外部最小化步骤则训练模型在此类挑战性条件下仍能生成高质量的移除结果。最终，我们的方法仅需6个采样步骤便实现了最先进的视频目标移除结果，且不依赖CFG，显著提升了推理效率。大量实验证明了 MiniMax-Remover 相比现有方法的有效性和优越性。代码和视频可在 https://minimax-remover.github.io 获取。
英文摘要：
Recent advances in video diffusion models have driven rapid progress in video editing techniques. However, video object removal, a critical subtask of video editing, remains challenging due to issues such as hallucinated objects and visual artifacts. Furthermore, existing methods often rely on computationally expensive sampling procedures and classifier-free guidance (CFG), resulting in slow inference. To address these limitations, we propose MiniMax-Remover, a novel two-stage video object removal approach. Motivated by the observation that text condition is not best suited for this task, we simplify the pretrained video generation model by removing textual input and cross-attention layers, resulting in a more lightweight and efficient model architecture in the first stage. In the second stage, we distilled our remover on successful videos produced by the stage-1 model and curated by human annotators, using a minimax optimization strategy to further improve editing quality and inference speed. Specifically, the inner maximization identifies adversarial input noise ("bad noise") that makes failure removals, while the outer minimization step trains the model to generate high-quality removal results even under such challenging conditions. As a result, our method achieves a state-of-the-art video object removal results with as few as 6 sampling steps and doesn't rely on CFG, significantly improving inference efficiency. Extensive experiments demonstrate the effectiveness and superiority of MiniMax-Remover compared to existing methods. Codes and Videos are available at: https://minimax-remover.github.io.
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。
