AI

ALIVE: Interaction-Aligned Object Insertion for First-Frame-Guided Video Editing

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Briefings

ALIVE is a framework enabling first-frame-guided video editing where inserted objects participate in coherent interactions with source video content. The system curates 35,800 editing pairs from diverse video sources and trains a vision-language model to predict interaction guidance, achieving a 43.9% improvement over baseline on interaction fidelity benchmarks.

Key points

• ALIVE framework uses an edited first frame and an instruction naming only the added object to make inserted objects participate in coherent interactions with source video contents.
• The system curates 35,800 editing pairs combining 3D-rendered, model-generated, and real-world videos with general editing pairs from ROSE to teach coordinated object behavior and source preservation.

Context

This research addresses a gap in video editing where inserted objects often fail to interact naturally with the source video, requiring significant manual adjustment. The ALIVE benchmark and VLM-based guidance provide a standardized method to evaluate and improve object insertion fidelity.

Limitations

Evaluation relies on a single VLM-based protocol and general benchmarks; specific interaction fidelity metrics remain unmeasured for all object types. Results are based on curated pairs and may not generalize to all video editing scenarios without further testing.

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