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FaceShot: Bring Any Character into Life
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FaceShot: Bring Any Character into Life
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In this paper, we present FaceShot, a novel training-free portrait animation framework designed to bring any character into life from any driven video without fine-tuning or retraining. We achieve this by offering precise and robust reposed landmark sequences from an appearance-guided landmark matching module and a coordinate-based landmark retargeting module. Together, these components harness the robust semantic correspondences of latent diffusion models to produce facial motion sequence across a wide range of character types. After that, we input the landmark sequences into a pre-trained landmark-driven animation model to generate animated video. With this powerful generalization capability, FaceShot can significantly extend the application of portrait animation by breaking the limitation of realistic portrait landmark detection for any stylized character and driven video. Also, FaceShot is compatible with any landmark-driven animation model, significantly improving overall performance. Extensive experiments on our newly constructed character benchmark CharacBench confirm that FaceShot consistently surpasses state-of-the-art (SOTA) approaches across any character domain. More results are available at our project website https://faceshot2024.github.io/faceshot/.
Forward citations
Cited by 7 Pith papers
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Eulerian Motion Guidance: Robust Image Animation via Bidirectional Geometric Consistency
Eulerian adjacent-frame motion guidance plus bidirectional geometric consistency improves training speed, temporal coherence, and artifact reduction in diffusion-based image animation.
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Eulerian Motion Guidance: Robust Image Animation via Bidirectional Geometric Consistency
Eulerian adjacent-frame motion fields with bidirectional cycle consistency checks enable faster parallel training and fewer artifacts in diffusion model image animation compared to initial-frame Lagrangian guidance.
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Eulerian Motion Guidance: Robust Image Animation via Bidirectional Geometric Consistency
Eulerian adjacent-frame motion guidance plus bidirectional geometric consistency yields faster training and more coherent diffusion-based image animation than first-frame reference methods.
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Eulerian Motion Guidance: Robust Image Animation via Bidirectional Geometric Consistency
Introduces Eulerian motion guidance with bidirectional geometric consistency to improve training speed and temporal quality in diffusion-based image animation.
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Hidden-Shot: Towards One-Shot Task Generalization for Low-Level Vision Generalist Models
Hidden-Shot adds an implicit visual-task prompt and selective merging step to existing low-level vision generalist models, paired with a 3C4U/3C7U evaluation framework that reports outperformance on seven and ten data...
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MegaStyle: Constructing Diverse and Scalable Style Dataset via Consistent Text-to-Image Style Mapping
A scalable pipeline generates an intra-consistent, inter-diverse 1.4M style image dataset from text-to-image models and uses it to train a style encoder and generalizable style transfer model.
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Eulerian Motion Guidance: Robust Image Animation via Bidirectional Geometric Consistency
Adjacent-frame Eulerian optical-flow guidance plus bidirectional geometric consistency is claimed to accelerate training and reduce drift in diffusion-based image animation versus Lagrangian baselines.
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