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MikuDance: Animating Character Art with Mixed Motion Dynamics
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We propose MikuDance, a diffusion-based pipeline incorporating mixed motion dynamics to animate stylized character art. MikuDance consists of two key techniques: Mixed Motion Modeling and Mixed-Control Diffusion, to address the challenges of high-dynamic motion and reference-guidance misalignment in character art animation. Specifically, a Scene Motion Tracking strategy is presented to explicitly model the dynamic camera in pixel-wise space, enabling unified character-scene motion modeling. Building on this, the Mixed-Control Diffusion implicitly aligns the scale and body shape of diverse characters with motion guidance, allowing flexible control of local character motion. Subsequently, a Motion-Adaptive Normalization module is incorporated to effectively inject global scene motion, paving the way for comprehensive character art animation. Through extensive experiments, we demonstrate the effectiveness and generalizability of MikuDance across various character art and motion guidance, consistently producing high-quality animations with remarkable motion dynamics.
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Cited by 2 Pith papers
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MagicAnime: A Hierarchically Annotated, Multimodal and Multitasking Dataset with Benchmarks for Cartoon Animation Generation
MagicAnime is a 400k-clip multimodal cartoon dataset with hierarchical annotations and benchmarks for image-to-video, pose-driven, face reenactment, and audio-driven animation generation.
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DreamDance: Animating Character Art via Inpainting Stable Gaussian Worlds
DreamDance animates a single character artwork by reconstructing its background as a 3D Gaussian scene and then inpainting the animated character into the rendered video.
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