The framework employs Synergistic Pose Modulation Modules to generate an adaptive and coherent pose representation that is highly compatible with the reference image. This allows for high-fidelity and coherent video generation starting directly from the reference state. SteadyDancer also utilizes a Staged Decoupled-Objective Training Pipeline that hierarchically optimizes the model for motion fidelity, visual quality, and temporal coherence.
SteadyDancer has been evaluated on various benchmarks, including the X-Dance and RealisDance-Val benchmarks. These benchmarks focus on spatio-temporal misalignments, visual identity preservation, temporal coherence, and motion accuracy. The results 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. This makes it a robust and efficient solution for human image animation tasks.
