SteadyDancer: Harmonized and Coherent Human Image Animation with First-Frame Preservation
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 Decoupled-Objective Training Pipeline that hierarchically optimizes the model for motion fidelity, visual quality, and temporal coherence. Experiments demonstrate that SteadyDancer achieves state-of-the-art performance in both appearance fidelity and motion control, while requiring significantly fewer training resources than comparable methods.
692525d916eb3a9f13103974
Jiaming Zhang
claimed_verified
2025-11-25T12:10:05.653Z
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Jiaming Zhang
jiamingZ
user
jiamingZ
692525d916eb3a9f13103975
Shengming Cao
692525d916eb3a9f13103976
Rui Li
692525d916eb3a9f13103977
Xiaotong Zhao
692525d916eb3a9f13103978
Yutao Cui
692525d916eb3a9f13103979
Xinglin Hou
692525d916eb3a9f1310397a
Gangshan Wu
692525d916eb3a9f1310397b
Haolan Chen
692525d916eb3a9f1310397c
Yu Xu
692525d916eb3a9f1310397d
Limin Wang
692525d916eb3a9f1310397e
Kai Ma
SteadyDancer: Harmonized and Coherent Human Image Animation with First-Frame Preservation
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 Decoupled-Objective Training Pipeline that hierarchically optimizes the model for motion fidelity, visual quality, and temporal coherence. Experiments demonstrate that SteadyDancer achieves state-of-the-art performance in both appearance fidelity and motion control, while requiring significantly fewer training resources than comparable methods.
SteadyDancer, an Image-to-Video framework, ensures first-frame identity preservation and precise motion control through harmonized conditions, adaptive pose representation, and hierarchical training objectives.
Image-to-Motion Binding Reference-to-Video Image-to-Video Condition-Reconciliation Mechanism Synergistic Pose Modulation Modules Staged Decoupled-Objective Training Pipeline motion fidelity visual quality temporal coherence