Key-point Guided Deformable Image Manipulation Using Diffusion Model
CoRR(2024)
摘要
In this paper, we introduce a Key-point-guided Diffusion probabilistic Model
(KDM) that gains precise control over images by manipulating the object's
key-point. We propose a two-stage generative model incorporating an optical
flow map as an intermediate output. By doing so, a dense pixel-wise
understanding of the semantic relation between the image and sparse key point
is configured, leading to more realistic image generation. Additionally, the
integration of optical flow helps regulate the inter-frame variance of
sequential images, demonstrating an authentic sequential image generation. The
KDM is evaluated with diverse key-point conditioned image synthesis tasks,
including facial image generation, human pose synthesis, and echocardiography
video prediction, demonstrating the KDM is proving consistency enhanced and
photo-realistic images compared with state-of-the-art models.
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