Zero-Shot Dynamic Concept Personalization with Grid-Based LoRA

• URL: http://arxiv.org/abs/2507.17963v1
• Date: Wed, 23 Jul 2025 22:09:38 GMT
• Title: Zero-Shot Dynamic Concept Personalization with Grid-Based LoRA
• Authors: Rameen Abdal, Or Patashnik, Ekaterina Deyneka, Hao Chen, Aliaksandr Siarohin, Sergey Tulyakov, Daniel Cohen-Or, Kfir Aberman,
• Abstract summary: We introduce a zero-shot framework for dynamic concept personalization in text-to-video models.<n>Our method leverages structured 2x2 video grids that spatially organize input and output pairs.<n>A dedicated Grid Fill module completes partially observed layouts, producing temporally coherent and identity preserving outputs.
• Score: 84.89284738178932
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in text-to-video generation have enabled high-quality synthesis from text and image prompts. While the personalization of dynamic concepts, which capture subject-specific appearance and motion from a single video, is now feasible, most existing methods require per-instance fine-tuning, limiting scalability. We introduce a fully zero-shot framework for dynamic concept personalization in text-to-video models. Our method leverages structured 2x2 video grids that spatially organize input and output pairs, enabling the training of lightweight Grid-LoRA adapters for editing and composition within these grids. At inference, a dedicated Grid Fill module completes partially observed layouts, producing temporally coherent and identity preserving outputs. Once trained, the entire system operates in a single forward pass, generalizing to previously unseen dynamic concepts without any test-time optimization. Extensive experiments demonstrate high-quality and consistent results across a wide range of subjects beyond trained concepts and editing scenarios.

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