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PIA: Your Personalized Image Animator via Plug-and-Play Modules in Text-to-Image Models

Yiming Zhang · Zhening Xing · Yanhong Zeng · Youqing Fang · Kai Chen

Arch 4A-E Poster #286
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Wed 19 Jun 5 p.m. PDT — 6:30 p.m. PDT


Recent advancements in personalized text-to-image (T2I) models have revolutionized content creation, empowering non-experts to generate stunning images with unique styles. While promising, adding realistic motions into these personalized images by text poses significant challenges in preserving distinct styles, high-fidelity details, and achieving motion controllability by text. In this paper, we present PIA, a Personalized Image Animator that excels in aligning with condition images, achieving motion controllability by text, and compatibility with various personalized T2I models without specific tuning. To achieve these goals, PIA builds upon a base T2I model with well-trained temporal alignment layers, allowing for the seamless transformation of any personalized T2I model into an image animation model. A key component of PIA is the introduction of the condition module, which utilizes the condition frame and inter-frame affinity as input to transfer appearance information guided by the affinity hint for individual frame synthesis in the latent space. This design mitigates the challenges of appearance-related image alignment within PIA and allows for a stronger focus on aligning with motion-related guidance. To address the lack of a benchmark for this field, we introduce AnimateBench, a comprehensive benchmark comprising diverse personalized T2I models, curated images, and motion-related prompts. We show extensive experiments on AnimateBench to verify the superiority of PIA. We will make our codes and models publicly available.

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