SteadyDancer: Harmonized and Coherent Human Image Animation with First-Frame Preservation
Jiaming Zhang, Shengming Cao, Rui Li, Xiaotong Zhao, Yutao Cui, Xinglin Hou, Gangshan Wu, Haolan Chen, Yu Xu, Limin Wang, Kai Ma

TL;DR
SteadyDancer is a novel human image animation framework that robustly preserves the first frame's identity while providing precise, coherent motion control through a new image-to-video paradigm.
Contribution
It introduces a first-of-its-kind framework with a condition-reconciliation mechanism, synergistic pose modulation, and a staged training pipeline for improved animation fidelity and coherence.
Findings
Achieves state-of-the-art appearance fidelity and motion control.
Requires fewer training resources than comparable methods.
Successfully preserves first-frame identity in animations.
Abstract
Preserving first-frame identity while ensuring precise motion control is a fundamental challenge in human image animation. The Image-to-Motion Binding process of the dominant Reference-to-Video (R2V) paradigm overlooks critical spatio-temporal misalignments common in real-world applications, leading to failures such as identity drift and visual artifacts. We introduce SteadyDancer, an Image-to-Video (I2V) paradigm-based framework that achieves harmonized and coherent animation and is the first to ensure first-frame preservation robustly. Firstly, we propose a Condition-Reconciliation Mechanism to harmonize the two conflicting conditions, enabling precise control without sacrificing fidelity. Secondly, we design Synergistic Pose Modulation Modules to generate an adaptive and coherent pose representation that is highly compatible with the reference image. Finally, we employ a Staged…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Human Motion and Animation · 3D Shape Modeling and Analysis
