Animate-X: Universal Character Image Animation with Enhanced Motion
  Representation
Authors: name: Shuai Tan, name: Biao Gong, name: Xiang Wang, name: Shiwei Zhang, name: Dandan Zheng, name: Ruobing Zheng, name: Kecheng Zheng, name: Jingdong Chen, name: Ming Yang

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.