Summary
SAMA is a framework for instruction-based video editing that factorizes the task into two components: semantic anchoring, which preserves the identity and semantic content of the input video, and motion modeling/alignment, which captures temporal dynamics. According to the paper (arXiv:2503.16897) by Xinyao Zhang, Wenkai Dong, and Yuxin Song, released on March 19, 2026 in the computer vision field, SAMA achieves state-of-the-art performance among open-source video editing models and is competitive with leading commercial systems such as Kling-Omni. The factorized design separates what should stay consistent in an edited video (semantics) from what should change (motion), offering a simple yet effective decomposition for controllable video editing. The full paper is available on arXiv. This post was automatically collected from a Chinese tech forum on March 22, 2026.
Paper Overview
Field: Computer Vision (CV)
Authors: Xinyao Zhang, Wenkai Dong, Yuxin Song
Released: 2026-03-19
arXiv: 2503.16897
Abstract
We present SAMA, a framework that factorizes video editing into semantic anchoring and motion modeling. SAMA achieves state-of-the-art performance among open-source models and is competitive with leading commercial systems like Kling-Omni.
Key Points
- Factorized design: SAMA decomposes instruction-based video editing into two subtasks — semantic anchoring (preserving the content/identity of the source video) and motion modeling/alignment (capturing temporal dynamics).
- Strong open-source results: Reported as state-of-the-art among open-source video editing models.
- Competitive with commercial systems: Matches the performance of leading commercial platforms such as Kling-Omni.
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*Auto-collected on 2026-03-22.*
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