CamDirector: Towards Long-Term Coherent Video Trajectory Editing

2026-03-04 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision, Emerging Technologies & Innovation · Depth: Expert, extended

Summary

CamDirector is a novel video trajectory editing (VTE) framework designed to synthesize new videos that follow user-defined camera paths while preserving scene content and plausibly inpainting unseen regions. It addresses limitations in existing VTE methods, such as imprecise camera control and long-range consistency issues, by introducing a hybrid warping scheme and a history-guided autoregressive diffusion model. The hybrid warping explicitly aggregates information across the entire source video, fusing static regions into a world cache and directly warping dynamic regions to guide refinement. The history-guided autoregressive model processes video segments jointly with their history, incrementally updating the world cache to reinforce inpainted content and ensure long-term temporal coherence. The framework achieves state-of-the-art performance with fewer parameters, as demonstrated on the new iPhone-PTZ benchmark, which features diverse camera motions and larger trajectory variations than previous datasets.

Key takeaway

For research scientists developing video generation or editing systems, CamDirector's approach to long-term temporal coherence and precise camera control offers a robust solution. You should consider integrating hybrid warping and history-guided autoregressive generation, particularly the progressive world cache update, to overcome challenges in maintaining consistency across extended video sequences and handling diverse camera motions.

Key insights

CamDirector enhances video trajectory editing with precise camera control and long-term consistency via hybrid warping and history-guided autoregressive diffusion.

Principles

• Decouple static and dynamic regions for efficient warping.
• Aggregate global scene information via a world cache.
• Maintain long-term coherence through history-guided autoregression.

Method

The method uses a hybrid warping scheme to create coarse frames by fusing a progressively updated world cache for static regions and directly warped dynamic regions. These coarse frames guide a history-guided autoregressive diffusion model for refinement and long-video generation.

In practice

• Utilize a world cache for global scene consistency.
• Employ history-guided autoregression for long video generation.
• Decouple static and dynamic scene elements for processing.

Topics

• Video Trajectory Editing
• Diffusion Models
• Hybrid Warping
• Autoregressive Generation
• Temporal Coherence

Best for: Research Scientist, AI Researcher, AI Scientist, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.