💡 The paper proposes a novel approach called Warp-as-History for camera-controlled video generation. Existing methods for this task typically require large-scale camera-annotated videos for post-training or rely on test-time optimization, which can be time-consuming and costly. The proposed method addresses this problem by transforming camera-induced warps into pseudo-history representations, which enables a frozen video generation model to follow camera trajectories without any training or test-time optimization.

The Warp-as-History method works by constructing camera-warped pseudo-history from past observations and feeding it through the model's visual-history pathway. The positional encoding is aligned with the target frames being denoised, and warped-history tokens without valid source observations are removed. This simple interface reveals a non-trivial zero-shot capability of the model to follow camera trajectories.

The results show that the proposed method can achieve good camera adherence, visual quality, and motion dynamics without requiring large-scale camera-annotated videos or test-time optimization. Furthermore, lightweight offline finetuning on only one camera-annotated video can further improve the model's capability and generalize to unseen videos. Extensive experiments on diverse datasets confirm the effectiveness of the Warp-as-History method, making it a promising approach for camera-controlled video generation.

Overall, the paper's contributions include a novel method for camera-controlled video generation that requires minimal training data and no test-time optimization, and demonstrates the potential for zero-shot capability in video generation models. The proposed approach has the potential to simplify the process of camera-controlled video generation and make it more accessible to a wider range of applications.