Ingredients: Blending Custom Photos with Video Diffusion Transformers

• URL: http://arxiv.org/abs/2501.01790v1
• Date: Fri, 03 Jan 2025 12:45:22 GMT
• Title: Ingredients: Blending Custom Photos with Video Diffusion Transformers
• Authors: Zhengcong Fei, Debang Li, Di Qiu, Changqian Yu, Mingyuan Fan,
• Abstract summary: texttIngredients is a framework to customize video creations incorporating multiple specific identity (ID) photos. Our method consists of three primary modules: (textbfi) a facial extractor that captures versatile and precise facial features for each human ID from both global and local perspectives. texttIngredients demonstrates superior performance in turning custom photos into dynamic and personalized video content.
• Score: 31.736838809714726
• License:
• Abstract: This paper presents a powerful framework to customize video creations by incorporating multiple specific identity (ID) photos, with video diffusion Transformers, referred to as \texttt{Ingredients}. Generally, our method consists of three primary modules: (\textbf{i}) a facial extractor that captures versatile and precise facial features for each human ID from both global and local perspectives; (\textbf{ii}) a multi-scale projector that maps face embeddings into the contextual space of image query in video diffusion transformers; (\textbf{iii}) an ID router that dynamically combines and allocates multiple ID embedding to the corresponding space-time regions. Leveraging a meticulously curated text-video dataset and a multi-stage training protocol, \texttt{Ingredients} demonstrates superior performance in turning custom photos into dynamic and personalized video content. Qualitative evaluations highlight the advantages of proposed method, positioning it as a significant advancement toward more effective generative video control tools in Transformer-based architecture, compared to existing methods. The data, code, and model weights are publicly available at: \url{https://github.com/feizc/Ingredients}.

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