  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
the 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
and allows for a stronger focus on aligning with motion-related guidance.
