Abstract
Video (camera) trajectory editing aims to synthesize new videos that follow user-defined camera paths while preserving scene content and plausibly inpainting previously unseen regions, upgrading amateur footage into professionally styled videos. Existing VTE methods struggle with precise camera control and long-range consistency because they either inject target poses through a limited-capacity embedding or rely on single-frame warping with only implicit cross-frame aggregation in video diffusion models. To address these issues, we introduce a new VTE framework that 1) explicitly aggregates information across the entire source video via a hybrid warping scheme. Specifically, static regions are progressively fused into a world cache then rendered to target camera poses, while dynamic regions are directly warped; their fusion yields globally consistent coarse frames that guide refinement. 2) processes video segments jointly with their history via a history-guided autoregressive diffusion model, while the world cache is incrementally updated to reinforce already inpainted content, enabling long-term temporal coherence. Finally, we present iPhone-PTZ, a new VTE benchmark with diverse camera motions and large trajectory variations, and achieve state-of-the-art performance with fewer parameters.
Abstract (translated)
视频（相机）轨迹编辑的目标是根据用户定义的摄像机路径合成新的视频，同时保持场景内容并合理地填补之前未见的区域，从而将业余拍摄的素材升级为专业风格的视频。现有的VTE方法在精确控制摄像机和长跨度一致性方面存在困难：它们要么通过有限容量的嵌入方式注入目标姿态，要么依赖于仅具有隐式跨帧聚合功能的单帧扭曲技术，在视频扩散模型中使用这些方法时效果不佳。 为了应对这些问题，我们引入了一种新的VTE框架： 1) 该框架明确地在整个源视频上进行信息聚合，采用了混合扭曲方案。具体来说，静态区域逐步融合到一个世界缓存区中，并根据目标摄像机姿态进行渲染；动态区域则直接被扭曲处理；两者融合后生成全局一致的粗略帧，这些帧引导进一步细化。 2) 该框架通过历史导向的自回归扩散模型同时处理视频片段及其历史记录。与此同时，世界缓存会逐步更新，以强化已经填充的内容，从而实现长时间的一致性。 最后，我们介绍了iPhone-PTZ，这是一个新的VTE基准测试平台，它包括多样化的相机运动和大的轨迹变化，并且通过更少的参数实现了最先进的性能。
URL
https://arxiv.org/abs/2603.02256
