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

Current instruction-guided video editing models struggle tosimultaneously balance precise semantic modifications with faithful mo-tion preservation. While existing approaches rely on injecting explicitexternal priors (e.g., VLM features or structural conditions) to mitigatethese issues, this reliance severely bottlenecks model robustness and gen-eralization. To overcome this limitation, we present SAMA (factorizedSemantic Anchoring and Motion Alignment), a framework that factor-ize video editing into semantic anchoring and motion modeling. First, weintroduce Semantic Anchoring which establish a reliable visual anchorby jointly predicting semantic tokens and video latents at sparse an-chor frames, enabling purely instruction-aware structural planning. Sec-ond, Motion Alignment pre-trains the same backbone on motion-centricvideo restoration pretext tasks (cube inpainting, speed perturbation, andtube shuffle), enabling the model to internalize temporal dynamics di-rectly from raw videos. SAMA is optimized with a two-stage pipeline: afactorized pre-training stage that learns inherent semantic-motion rep-resentations without paired video-instruction editing data, followed bysupervised fine-tuning on paired editing data. Remarkably, the factorizedpre-training alone already yields strong zero-shot video editing ability,validating the proposed factorization. SAMA achieves state-of-the-artperformance among open-source models and is competitive with leadingcommercial systems (e.g. Kling-Omni). Code, models, and datasets arereleased in https://cynthiazxy123.github.io/SAMA.