InstructMix2Mix：通过多视图模型个性化实现一致的稀疏视图编辑
InstructMix2Mix: Consistent Sparse-View Editing Through Multi-View Model Personalization
AI总结：
针对稀疏视图输入的多视图图像编辑任务，提出InstructMix2Mix框架，将2D扩散模型的编辑能力蒸馏到预训练多视图扩散模型中，通过增量更新、专用调度器等适配，显著提升跨视图一致性与编辑质量。
摘要：
我们研究从稀疏输入视图进行多视图图像编辑的任务，输入可视为从不同视角捕捉场景的图像混合体。目标是根据文本指令修改场景，同时保留所有视图间的一致性。现有基于场景专属神经场或时间注意力机制的方法在此设置下表现不佳，常产生伪影和不连贯编辑。我们提出InstructMix2Mix（I-Mix2Mix）框架，将2D扩散模型的编辑能力蒸馏到预训练多视图扩散模型中，利用其数据驱动的3D先验确保跨视图一致性。关键贡献是用多视图扩散学生模型替代Score Distillation Sampling（SDS）中传统的神经场整合器，涉及新适配：时间步增量学生更新、防止退化的专用教师噪声调度器、无额外成本增强跨视图连贯性的注意力修改。实验表明，I-Mix2Mix显著提升多视图一致性，同时保持高单帧编辑质量。
英文摘要：
We address the task of multi-view image editing from sparse input views, where the inputs can be seen as a mix of images capturing the scene from different viewpoints. The goal is to modify the scene according to a textual instruction while preserving consistency across all views. Existing methods, based on per-scene neural fields or temporal attention mechanisms, struggle in this setting, often producing artifacts and incoherent edits. We propose InstructMix2Mix (I-Mix2Mix), a framework that distills the editing capabilities of a 2D diffusion model into a pretrained multi-view diffusion model, leveraging its data-driven 3D prior for cross-view consistency. A key contribution is replacing the conventional neural field consolidator in Score Distillation Sampling (SDS) with a multi-view diffusion student, which requires novel adaptations: incremental student updates across timesteps, a specialized teacher noise scheduler to prevent degeneration, and an attention modification that enhances cross-view coherence without additional cost. Experiments demonstrate that I-Mix2Mix significantly improves multi-view consistency while maintaining high per-frame edit quality.
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。
