SAMA: Factorized Semantic Anchoring and Motion Alignment for Instruction-Guided Video Editing
SAMA achieves instruction-guided video editing through semantic anchoring and motion alignment, significantly enhancing editing precision and motion consistency.
Key Findings
Methodology
SAMA framework decomposes video editing into semantic anchoring and motion modeling. Semantic Anchoring establishes reliable visual anchors by predicting semantic tokens and video latents at sparse anchor frames, enabling purely instruction-aware structural planning. Motion Alignment pre-trains the same backbone on motion-centric video restoration tasks, allowing the model to internalize temporal dynamics directly from raw videos.
Key Results
- SAMA achieved a 9.422 instruction-following score and an 8.244 quality score on VIE-Bench, significantly outperforming other open-source models.
- On OpenVE-Bench, SAMA excelled across multiple task categories, achieving top scores in Swap/Change and Remove tasks.
- On ReCo-Bench, SAMA's overall score was 8.92, demonstrating strong cross-scenario editing capabilities.
Significance
SAMA holds significant implications for academia and industry. It addresses the longstanding challenge of balancing semantic modifications with motion preservation, offering a solution that does not rely on external priors, thus enhancing model robustness and generalization.
Technical Contribution
SAMA's technical contributions lie in its innovative decomposition strategy, significantly enhancing editing precision and consistency through semantic anchoring and motion alignment. Unlike existing methods, SAMA does not rely on external priors, providing new theoretical guarantees and engineering possibilities.
Novelty
SAMA is the first to decompose video editing into semantic anchoring and motion modeling as independent capabilities, offering an innovative approach without external priors, fundamentally differing from existing methods.
Limitations
- SAMA faces challenges in handling fast motion and complex camera dynamics, potentially leading to background blurring.
- In zero-shot settings, attribute edits may be temporally inconsistent, and newly added objects may appear slightly blurry.
Future Work
Future research directions include long-video editing, fast-motion scenarios, and stronger semantic tokenization to further reduce residual artifacts and temporal inconsistencies.
AI Executive Summary
Current instruction-guided video editing models struggle to balance precise semantic modifications with faithful motion preservation. Existing approaches rely on injecting explicit external priors (e.g., VLM features or structural conditions) to mitigate these issues, but 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.
Deep Analysis
Background
The field of video editing has seen significant advancements in recent years, particularly in instruction-guided editing tasks. Early methods primarily relied on extensions of image editing techniques but faced limitations in temporal consistency and semantic precision. With the emergence of large-scale datasets like Señorita-2M and InsViE-1M, research has gradually shifted towards data-driven end-to-end video editing models. However, these models often depend on external priors, such as VLM features or structural signals, which limit model robustness and generalization.
Core Problem
Existing instruction-guided video editing models struggle to balance precise semantic modifications with faithful motion preservation. Aggressive semantic changes can induce localized artifacts, identity drift, and texture popping, while enforcing temporal consistency can dilute the intended edit and reduce instruction fidelity.
Innovation
SAMA's core innovation lies in its decomposition strategy, splitting video editing into semantic anchoring and motion modeling as independent capabilities. Semantic Anchoring predicts semantic tokens and video latents at sparse anchor frames, enabling purely instruction-aware structural planning. Motion Alignment pre-trains on motion-centric video restoration tasks, allowing the model to internalize temporal dynamics directly from raw videos.
Methodology
- �� Semantic Anchoring: Predicts semantic tokens and video latents at sparse anchor frames for instruction-aware structural planning.
- �� Motion Alignment: Pre-trains on motion-centric video restoration tasks to internalize temporal dynamics.
- �� Two-stage optimization: Factorized pre-training learns inherent semantic-motion representations, followed by supervised fine-tuning on paired editing data.
Experiments
The experimental design includes evaluations on multiple benchmark datasets such as VIE-Bench, OpenVE-Bench, and ReCo-Bench. Metrics used include instruction-following, quality, temporal stability, etc. Ablation studies were conducted to validate the effectiveness of semantic anchoring and motion alignment.
Results
SAMA achieved a 9.422 instruction-following score and an 8.244 quality score on VIE-Bench, significantly outperforming other open-source models. On OpenVE-Bench, SAMA excelled across multiple task categories, achieving top scores in Swap/Change and Remove tasks. On ReCo-Bench, SAMA's overall score was 8.92, demonstrating strong cross-scenario editing capabilities.
Applications
SAMA can be directly applied in video editing software to enhance user experience. Its independence from external priors makes it widely applicable in various scenarios such as film production, advertising design, etc.
Limitations & Outlook
Despite SAMA's outstanding performance on multiple benchmarks, it still faces challenges in handling fast motion and complex camera dynamics, potentially leading to background blurring. Additionally, in zero-shot settings, attribute edits may be temporally inconsistent, and newly added objects may appear slightly blurry. Future research directions include long-video editing, fast-motion scenarios, and stronger semantic tokenization to further reduce residual artifacts and temporal inconsistencies.
Plain Language Accessible to non-experts
Imagine you're in a kitchen cooking a meal. You have a recipe (instruction) and need to change some ingredients (semantic modification) without rearranging the kitchen layout (motion preservation). SAMA is like a smart kitchen assistant that helps you make these ingredient changes accurately without disrupting the kitchen's order. First, it places markers at key locations in the kitchen (anchor frames) to ensure you know which ingredients are needed for each step. Then, as you cook, it ensures all steps follow the recipe's rhythm, preventing any changes from disrupting the overall process. This way, you can make all the ingredient modifications without affecting the overall layout.
ELI14 Explained like you're 14
Hey there! Imagine you're playing a video game where you have to change some elements in the game, like changing a character's outfit color to red, while keeping the game running smoothly. SAMA is like a super helper in the game that lets you make these changes without affecting the game's flow. First, it places markers at key spots in the game to show you where changes are needed. Then, as you make changes, it ensures everything runs smoothly, so no changes mess up the game. It's like having a magic wand that makes everything work perfectly! Cool, right?
Glossary
Semantic Anchoring
Predicting semantic tokens and video latents at sparse anchor frames for instruction-aware structural planning.
Used in SAMA to achieve purely instruction-aware structural planning.
Motion Alignment
Pre-training on motion-centric video restoration tasks to internalize temporal dynamics.
Used in SAMA to enhance temporal consistency.
Instruction-Guided
Editing an input video following a text instruction.
SAMA's core task is instruction-guided video editing.
VIE-Bench
A benchmark dataset for evaluating video editing model performance.
SAMA was evaluated on VIE-Bench.
OpenVE-Bench
Another benchmark dataset for evaluating video editing model performance.
SAMA excelled on OpenVE-Bench.
ReCo-Bench
A benchmark dataset for evaluating video editing model performance across multiple task categories.
SAMA achieved high scores on ReCo-Bench.
Ablation Study
Evaluating the impact of removing or modifying certain parts of a model on overall performance.
SAMA conducted ablation studies to validate its components.
Zero-Shot
The ability to perform inference on new tasks without specific training data.
SAMA demonstrated strong zero-shot editing capabilities.
External Priors
External information or features used during model training or inference.
SAMA does not rely on external priors.
Temporal Consistency
Maintaining visual coherence between frames before and after editing in video editing.
SAMA enhances temporal consistency through motion alignment.
Open Questions Unanswered questions from this research
- 1 How can video editing models be made more robust and generalizable without relying on external priors? Current methods face challenges in handling fast motion and complex camera dynamics, necessitating stronger model capabilities to address these issues.
- 2 How can temporal consistency in attribute edits be improved in zero-shot settings? While SAMA demonstrates strong zero-shot editing capabilities, attribute edits may be temporally inconsistent in some cases.
- 3 How can residual artifacts and temporal inconsistencies in video editing be further reduced? SAMA has made progress in this area, but there is room for improvement.
- 4 How can efficient semantic anchoring and motion alignment be maintained in long-video editing? Long-video editing poses higher demands on computational resources and temporal consistency.
- 5 How can precision and consistency in video editing across multiple task categories be improved? SAMA performs well across multiple task categories, but there is potential for further enhancement.
Applications
Immediate Applications
Film Production
SAMA can be used in film production for video editing, enhancing editing precision and consistency while reducing manual intervention.
Advertising Design
In advertising design, SAMA can help designers quickly achieve complex visual effects, enhancing creative expression.
Educational Training
In educational training, SAMA can be used to create instructional videos, helping teachers better present educational content.
Long-term Vision
Intelligent Video Editing Software
SAMA can drive the development of intelligent video editing software, achieving more efficient automated editing processes.
Virtual Reality
In virtual reality, SAMA can be used for real-time video editing, enhancing user immersion.
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.