Animate-X++: Universal Character Image Animation with Dynamic Backgrounds

• URL: http://arxiv.org/abs/2508.09454v1
• Date: Wed, 13 Aug 2025 03:11:28 GMT
• Title: Animate-X++: Universal Character Image Animation with Dynamic Backgrounds
• Authors: Shuai Tan, Biao Gong, Zhuoxin Liu, Yan Wang, Xi Chen, Yifan Feng, Hengshuang Zhao,
• Abstract summary: Animate-X++ is a universal animation framework based on DiT for various character types, including anthropomorphic characters.<n>To enhance motion representation, we introduce the Pose Indicator, which captures comprehensive motion pattern from the driving video through both implicit and explicit manner.<n>For the second challenge, we introduce a multi-task training strategy that jointly trains the animation and TI2V tasks.
• Score: 32.04255747303296
• License: http://creativecommons.org/licenses/by/4.0/
• 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. Furthermore, previous methods could only generate videos with static backgrounds, which limits the realism of the videos. For the first challenge, 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 DiT for various character types, 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 DiT by simulating possible inputs in advance that may arise during inference. For the second challenge, we introduce a multi-task training strategy that jointly trains the animation and TI2V tasks. Combined with the proposed partial parameter training, this approach achieves not only character animation but also text-driven background dynamics, making the videos more realistic. Moreover, we introduce a new Animated Anthropomorphic Benchmark (A2Bench) to evaluate the performance of Animate-X++ on universal and widely applicable animation images. Extensive experiments demonstrate the superiority and effectiveness of Animate-X++.

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