SAMA: Factorized Semantic Anchoring and Motion Alignment for Instruction-Guided Video Editing

TL;DR

SAMA introduces a novel framework for instruction-guided video editing that factorizes semantic anchoring and motion modeling, improving robustness and generalization without relying on external priors.

Contribution

The paper proposes a factorized approach with semantic anchoring and motion alignment, enabling better zero-shot and supervised video editing performance.

Findings

Achieves state-of-the-art open-source video editing results

Strong zero-shot editing capabilities from pre-training alone

Competitive with leading commercial systems

Abstract

Current instruction-guided video editing models struggle to simultaneously balance precise semantic modifications with faithful motion preservation. While existing approaches rely on injecting explicit external priors (e.g., VLM features or structural conditions) to mitigate these issues, this reliance severely bottlenecks model robustness and generalization. To overcome this limitation, we present SAMA (factorized Semantic Anchoring and Motion Alignment), a framework that factorizes video editing into semantic anchoring and motion modeling. First, we introduce Semantic Anchoring, which establishes a reliable visual anchor by jointly predicting semantic tokens and video latents at sparse anchor frames, enabling purely instruction-aware structural planning. Second, Motion Alignment pre-trains the same backbone on motion-centric video restoration pretext tasks (cube inpainting, speed perturbation, and tube shuffle), enabling the model to internalize temporal dynamics directly from raw videos. SAMA is optimized with a two-stage pipeline: a factorized pre-training stage that learns inherent semantic-motion representations without paired video-instruction editing data, followed by supervised fine-tuning on paired editing data. Remarkably, the factorized pre-training alone already yields strong zero-shot video editing ability, validating the proposed factorization. SAMA achieves state-of-the-art performance among open-source models and is competitive with leading commercial systems (e.g., Kling-Omni). Code, models, and datasets will be released.