AnimateAnyMesh++: A Flexible 4D Foundation Model for High-Fidelity Text-Driven Mesh Animation

Problem
This paper addresses the limitations in high-fidelity animated 3D model generation, particularly the challenges associated with modeling spatio-temporal distributions and the scarcity of 4D training data. The authors present AnimateAnyMesh++, a preprint work that significantly enhances the capabilities of text-driven animation for arbitrary 3D meshes. The existing literature lacks comprehensive datasets and robust architectures that can effectively handle the complexities of 4D content generation, which this work aims to fill.

Method
AnimateAnyMesh++ introduces several key technical contributions:

1. Dataset Expansion: The authors expand the DyMesh-XL dataset by mining dynamic content from Objaverse-XL, increasing the number of unique identities from 60K to 300K. This expansion enhances category and motion diversity, providing a richer training foundation.
2. Architecture Redesign: The core architecture, DyMeshVAE-Flex, is redesigned to incorporate power-law topology-aware attention and vertex-normal enhanced features. These modifications improve trajectory reconstruction and local geometry preservation while reducing trajectory-sticking artifacts.
3. Variable-Length Sequence Support: Architectural changes to both DyMeshVAE-Flex and the rectified-flow (RF) generator enable the model to support variable-length sequence training and generation. This allows for the creation of longer animations without sacrificing reconstruction fidelity.

The training process leverages the enlarged DyMesh-XL dataset, and the model is designed to generate semantically accurate and temporally coherent mesh animations efficiently.

Results
AnimateAnyMesh++ demonstrates significant performance improvements over prior methods. The authors report that their model generates high-quality animations within seconds, outperforming existing baselines in terms of both quality and efficiency. Specific quantitative results include improvements in trajectory reconstruction accuracy and local geometry preservation, although exact numerical metrics and comparisons to specific baselines are not detailed in the abstract. The model’s performance is validated across various benchmarks and in-the-wild meshes, indicating its robustness and generalizability.

Limitations
The authors acknowledge that while AnimateAnyMesh++ shows substantial improvements, it still relies on the quality and diversity of the training data, which may limit its performance in scenarios with unseen mesh types or motion patterns. Additionally, the paper does not address potential computational costs associated with the expanded dataset and the complexity of the redesigned architecture, which may impact deployment in resource-constrained environments. The lack of detailed quantitative comparisons in the abstract also limits the ability to fully assess the model’s performance against specific state-of-the-art methods.

Why it matters
The advancements presented in AnimateAnyMesh++ have significant implications for the field of 4D content creation. By providing a flexible and efficient framework for generating high-fidelity animated meshes, this work paves the way for more sophisticated applications in gaming, virtual reality, and film production. The expanded dataset and improved architecture can serve as a foundation for future research, enabling further exploration of text-driven animation and the development of more complex 4D models.

Authors: Zijie Wu, Chaohui Yu, Fan Wang, Xiang Bai
Source: arXiv:2604.26917
URL: https://arxiv.org/abs/2604.26917v1