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

2025-08-12

AI Summary

This paper introduces the first general-purpose image animation framework for diverse anthropomorphic characters, addressing two key challenges in single-image-driven animation: poor cross-character generalization and static background artifacts. Methodologically, it (1) designs a Pose Indicator module to enhance pose representation; (2) fuses implicit CLIP semantic features with explicit motion inputs for robust cross-character modeling; and (3) proposes a multi-task DiT architecture that jointly optimizes character animation generation and text-guided dynamic background synthesis. The framework leverages CLIP visual encoding, partial parameter fine-tuning, and end-to-end joint training. Evaluated on the newly constructed A2Bench benchmark, it achieves significant improvements in cross-character animation quality (−12.7% FVD) and background motion fidelity (+28.4% motion score), substantially enhancing video photorealism and practical applicability.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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++.

Problem

• Limited generalization to anthropomorphic characters in animation
• Static backgrounds reduce realism in generated videos
• Insufficient motion modeling for diverse character types

Innovation

• DiT-based universal animation framework for diverse characters
• Pose Indicator for enhanced motion representation
• Multi-task training for dynamic backgrounds