FaceShot: Bring Any Character into Life

2025-03-02

AI Summary

This work addresses the limitations of existing portrait animation methods, which rely on real-face keypoint detectors and thus struggle to generalize to stylized characters or arbitrary driving videos. To this end, we propose FaceShot—a fine-tuning- and retraining-free cross-style portrait animation framework. Methodologically, FaceShot introduces two novel components: (1) an appearance-guided keypoint matching module and (2) a coordinate-based keypoint redirection module, both eliminating dependence on real-face keypoint detectors. Furthermore, it leverages latent diffusion models to achieve semantic-aware dense correspondence modeling, while remaining compatible with any pre-trained keypoint-driven animation model. Evaluated on our newly constructed CharacBench benchmark—a comprehensive evaluation suite for stylized character animation—FaceShot consistently outperforms state-of-the-art methods, delivering significant improvements in animation fidelity and cross-style generalization capability.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

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/.

Problem

• Enables portrait animation for any character without retraining.
• Generates facial motion sequences using semantic correspondences.
• Improves performance across diverse character types and styles.

Innovation

• Training-free portrait animation framework
• Appearance-guided landmark matching module
• Coordinate-based landmark retargeting module