Data-Efficient Training and Adaptation of Target-Image-Aware Video Editing Models

• Adeel Khan

  Department of Computer Science, University of Gujrat, Jalalpur Jattan Road, Gujrat 50700, Pakistan

  Author

• Sajid Rafiq

  Department of Information Technology, The University of Haripur, Hattar Road, Haripur 22620, Pakistan

  Author

Video editing models that can adapt the content of a source video to match the identity, appearance, or style specified by a target image are increasingly important for controllable content creation, personalization, and post-production workflows. However, such target-image-aware video editing models typically depend on large collections of paired data or extensive per-target fine-tuning, which limits their practicality in scenarios where only a few reference images and possibly a short video are available. This paper examines data-efficient training and adaptation strategies for target-image-aware video editing models that leverage strong pretrained video generators and parameter-efficient conditioning mechanisms. The study formulates target-image-aware editing as a conditional transformation problem, where a pretrained backbone is adapted using small sets of target images and limited video samples while preserving temporal consistency and source motion. Several loss components are combined to balance fidelity to the target appearance, adherence to the source motion, and robustness to distribution shift between training and deployment domains. The paper investigates low-rank adaptation, hypernetwork-based modulation, and regularization by distillation from the base generator as mechanisms for reducing data requirements while maintaining editing quality. A theoretical analysis considers generalization from few examples and the interaction between adapter capacity and sample complexity. Empirical investigations on generic video editing scenarios illustrate how different components affect identity preservation, temporal coherence, and robustness to occlusions and large motions. The discussion highlights practical trade-offs between adaptation speed, parameter count, and performance under strict data budgets, and outlines directions for further improving data efficiency in video editing systems.

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