Animate-X: Universal Character Image Animation with Enhanced Motion Representation
Shuai Tan, Biao Gong, Xiang Wang, Shiwei Zhang, Dandan Zheng, Ruobing Zheng, Kecheng Zheng, Jingdong Chen, Ming Yang
2024-10-15
Summary
This paper introduces Animate-X, a new framework for animating character images that can handle a wide variety of characters, including those that are not human, by improving how motion is represented.
What's the problem?
Most existing animation methods work well only for human figures and struggle with anthropomorphic characters (like animals or cartoon characters) commonly used in gaming and entertainment. This limitation is mainly due to their inability to accurately understand and represent the movements of these characters based on the reference videos, leading to rigid animations that don't look natural.
What's the solution?
Animate-X solves this problem by using a special tool called the Pose Indicator, which captures detailed motion patterns from driving videos in two ways: implicitly, by extracting general motion features, and explicitly, by simulating different possible movements. This allows the framework to create smoother and more realistic animations for a variety of character types. Additionally, the authors introduced a new benchmark called A^2Bench to test how well Animate-X performs in animating these diverse characters.
Why it matters?
This research is significant because it expands the possibilities for character animation beyond just human figures. By enabling realistic animations for a broader range of characters, Animate-X can enhance storytelling and visual experiences in video games, movies, and other media, making them more engaging and dynamic.
Abstract
Character image animation, which generates high-quality videos from a reference image and target pose sequence, has seen significant progress in recent years. However, most existing methods only apply to human figures, which usually do not generalize well on anthropomorphic characters commonly used in industries like gaming and entertainment. Our in-depth analysis suggests to attribute this limitation to their insufficient modeling of motion, which is unable to comprehend the movement pattern of the driving video, thus imposing a pose sequence rigidly onto the target character. To this end, this paper proposes Animate-X, a universal animation framework based on LDM for various character types (collectively named X), including anthropomorphic characters. To enhance motion representation, we introduce the Pose Indicator, which captures comprehensive motion pattern from the driving video through both implicit and explicit manner. The former leverages CLIP visual features of a driving video to extract its gist of motion, like the overall movement pattern and temporal relations among motions, while the latter strengthens the generalization of LDM by simulating possible inputs in advance that may arise during inference. Moreover, we introduce a new Animated Anthropomorphic Benchmark (A^2Bench) to evaluate the performance of Animate-X on universal and widely applicable animation images. Extensive experiments demonstrate the superiority and effectiveness of Animate-X compared to state-of-the-art methods.