Recent advances in 4D content generation have attracted increasing attention, yet creating high-quality animated 3D models remains challenging due to the complexity of modeling spatio-temporal distributions and the scarcity of 4D training data. We present AnimateAnyMesh++, a feed-forward framework for text-driven animation of arbitrary 3D meshes with substantial upgrades in data, architecture, and generative capability. First, we expand the DyMesh-XL dataset by mining dynamic content from Objaverse-XL, increasing the number of unique identities from 60K to 300K and substantially broadening category and motion diversity. Second, we redesign DyMeshVAE-Flex with power-law topology-aware attention and vertex-normal enhanced features, which significantly improves trajectory reconstruction, local geometry preservation, and mitigates trajectory-sticking artifacts. Third, we introduce architectural changes to both DyMeshVAE-Flex and the rectified-flow (RF) generator to support variable-length sequence training and generation, enabling longer animations while preserving reconstruction fidelity. Extensive experiments demonstrate that AnimateAnyMesh++ generates semantically accurate and temporally coherent mesh animations within seconds, surpassing prior approaches in quality and efficiency. The enlarged DyMesh-XL, the upgraded DyMeshVAE-Flex, and variable-length RF together deliver consistent gains across benchmarks and in-the-wild meshes. We will release code, models, and the expanded DyMesh-XL upon acceptance of this manuscript to facilitate research in 4D content creation.

翻译：近期4D内容生成的进展引起了越来越多的关注，然而由于时空分布建模的复杂性以及4D训练数据的稀缺性，创建高质量动画3D模型仍然具有挑战性。我们提出AnimateAnyMesh++，这是一个用于任意3D网格文本驱动动画的前馈框架，在数据、架构和生成能力方面进行了重大升级。首先，我们通过从Objaverse-XL中挖掘动态内容扩展了DyMesh-XL数据集，将唯一身份数量从6万增加到30万，显著拓宽了类别和运动多样性。其次，我们重新设计了DyMeshVAE-Flex，采用幂律拓扑感知注意力和顶点法线增强特征，显著改善了轨迹重建和局部几何保持，并减轻了轨迹粘连伪影。第三，我们对DyMeshVAE-Flex和整流流生成器引入架构更改，以支持可变长度序列训练和生成，在保持重建保真度的同时实现更长动画。大量实验表明，AnimateAnyMesh++能在数秒内生成语义准确且时间一致的网格动画，在质量和效率上超越了先前方法。扩展后的DyMesh-XL、升级后的DyMeshVAE-Flex以及可变长度整流流共同在基准测试和野外网格上取得了持续改进。我们将在本文被接收后发布代码、模型和扩展后的DyMesh-XL，以促进4D内容创作研究。