Low-Cost Test-Time Adaptation for Robust Video Editing

2025-07-29

AI Summary

Existing video editing methods suffer from temporal inconsistency and overfitting to text prompts, while relying heavily on large-scale annotated datasets and substantial computational resources. To address these challenges, we propose Vid-TTA, a low-cost test-time adaptation framework. Vid-TTA introduces motion-aware frame reconstruction and text prompt perturbation as self-supervised auxiliary tasks, enabling dynamic UNet optimization during inference. It further incorporates a meta-learning-driven dynamic loss balancing mechanism to achieve content-adaptive, personalized adjustments. Crucially, Vid-TTA requires no additional training or fine-tuning—only a single forward pass suffices for adaptive optimization. Experimental results demonstrate that Vid-TTA significantly improves temporal coherence and text-prompt generalization, delivering plug-and-play performance gains with minimal computational overhead. This establishes a new paradigm for high-quality video editing under resource-constrained conditions.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Search and Optimization: Learning to Search
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Video editing is a critical component of content creation that transforms raw footage into coherent works aligned with specific visual and narrative objectives. Existing approaches face two major challenges: temporal inconsistencies due to failure in capturing complex motion patterns, and overfitting to simple prompts arising from limitations in UNet backbone architectures. While learning-based methods can enhance editing quality, they typically demand substantial computational resources and are constrained by the scarcity of high-quality annotated data. In this paper, we present Vid-TTA, a lightweight test-time adaptation framework that personalizes optimization for each test video during inference through self-supervised auxiliary tasks. Our approach incorporates a motion-aware frame reconstruction mechanism that identifies and preserves crucial movement regions, alongside a prompt perturbation and reconstruction strategy that strengthens model robustness to diverse textual descriptions. These innovations are orchestrated by a meta-learning driven dynamic loss balancing mechanism that adaptively adjusts the optimization process based on video characteristics. Extensive experiments demonstrate that Vid-TTA significantly improves video temporal consistency and mitigates prompt overfitting while maintaining low computational overhead, offering a plug-and-play performance boost for existing video editing models.

Problem

• Addresses temporal inconsistencies in video editing due to complex motion patterns
• Reduces overfitting to simple prompts in UNet-based video editing models
• Provides low-cost adaptation for robust video editing without high resource demands

Innovation

• Self-supervised auxiliary tasks for video adaptation
• Motion-aware frame reconstruction mechanism
• Meta-learning driven dynamic loss balancing