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      "statement": "# Guo et al.: transferable motion modules for personalized diffusion models\n\nTrained a motion module on video while freezing a base text-to-image model, then inserted that module into compatible personalized variants to animate them without variant-specific video tuning.\n\n## Scientific scope\n\nDiffusion, DreamBooth, LoRA and temporal attention are inherited. No-specific-tuning means no extra tuning for each compatible derived model, not no initial motion training or universal compatibility. First two pages inspected.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nYuwei Guo; Ceyuan Yang; Anyi Rao; Yaohui Wang; Yu Qiao; Dahua Lin; Bo Dai\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://arxiv.org/abs/2307.04725v1)\n",
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