VEGGIE: Instructional Editing and Reasoning of Video Concepts with Grounded Generation

• URL: http://arxiv.org/abs/2503.14350v2
• Date: Wed, 19 Mar 2025 20:33:40 GMT
• Title: VEGGIE: Instructional Editing and Reasoning of Video Concepts with Grounded Generation
• Authors: Shoubin Yu, Difan Liu, Ziqiao Ma, Yicong Hong, Yang Zhou, Hao Tan, Joyce Chai, Mohit Bansal,
• Abstract summary: We introduce VEGGIE, a simple end-to-end framework that unifies video concept editing, grounding, and reasoning based on diverse user instructions.<n> VEGGIE shows strong performance in instructional video editing with different editing skills, outperforming the best instructional baseline as a versatile model.
• Score: 67.31149310468801
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent video diffusion models have enhanced video editing, but it remains challenging to handle instructional editing and diverse tasks (e.g., adding, removing, changing) within a unified framework. In this paper, we introduce VEGGIE, a Video Editor with Grounded Generation from Instructions, a simple end-to-end framework that unifies video concept editing, grounding, and reasoning based on diverse user instructions. Specifically, given a video and text query, VEGGIE first utilizes an MLLM to interpret user intentions in instructions and ground them to the video contexts, generating frame-specific grounded task queries for pixel-space responses. A diffusion model then renders these plans and generates edited videos that align with user intent. To support diverse tasks and complex instructions, we employ a curriculum learning strategy: first aligning the MLLM and video diffusion model with large-scale instructional image editing data, followed by end-to-end fine-tuning on high-quality multitask video data. Additionally, we introduce a novel data synthesis pipeline to generate paired instructional video editing data for model training. It transforms static image data into diverse, high-quality video editing samples by leveraging Image-to-Video models to inject dynamics. VEGGIE shows strong performance in instructional video editing with different editing skills, outperforming the best instructional baseline as a versatile model, while other models struggle with multi-tasking. VEGGIE also excels in video object grounding and reasoning segmentation, where other baselines fail. We further reveal how the multiple tasks help each other and highlight promising applications like zero-shot multimodal instructional and in-context video editing.

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