AnimateAnyMesh++: A Flexible 4D Foundation Model for High-Fidelity Text-Driven Mesh Animation
Zijie Wu, Chaohui Yu, Fan Wang, Xiang Bai

TL;DR
AnimateAnyMesh++ is a new framework that enables high-quality, text-driven 4D mesh animations with larger datasets, improved architecture, and support for longer sequences, outperforming previous methods in quality and speed.
Contribution
The paper introduces AnimateAnyMesh++, a comprehensive framework with an expanded dataset, enhanced architecture, and variable-length sequence support for superior 4D mesh animation.
Findings
Outperforms prior approaches in quality and efficiency.
Supports longer animations without loss of fidelity.
Enables semantically accurate and coherent mesh animations within seconds.
Abstract
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…
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