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

2026-04-29 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision & Pattern Recognition · Depth: Expert, quick

Summary

AnimateAnyMesh++ is a new feed-forward framework designed for text-driven animation of arbitrary 3D meshes, addressing challenges in 4D content generation like spatio-temporal modeling complexity and data scarcity. The framework introduces significant upgrades across its data, architecture, and generative capabilities. It expands the DyMesh-XL dataset by integrating dynamic content from Objaverse-XL, increasing unique identities from 60K to 300K and enhancing category and motion diversity. The DyMeshVAE-Flex architecture has been redesigned with power-law topology-aware attention and vertex-normal enhanced features, improving trajectory reconstruction and local geometry preservation while reducing artifacts. Additionally, AnimateAnyMesh++ incorporates architectural changes to both DyMeshVAE-Flex and its rectified-flow (RF) generator to support variable-length sequence training and generation, enabling longer animations with high fidelity. This system generates semantically accurate and temporally coherent mesh animations rapidly, outperforming previous methods in quality and efficiency.

Key takeaway

For research scientists working on 4D content creation, AnimateAnyMesh++ offers a robust solution for generating high-fidelity, text-driven mesh animations. You should explore its expanded DyMesh-XL dataset and architectural improvements, particularly the variable-length sequence generation, to overcome previous limitations in animation length and quality. This framework provides a significant advancement for developing more complex and diverse animated 3D models.

Key insights

AnimateAnyMesh++ is a 4D foundation model for high-fidelity, text-driven mesh animation.

Principles

• Data diversity improves 4D content generation.
• Topology-aware attention enhances mesh reconstruction.
• Variable-length training supports longer animations.

Method

AnimateAnyMesh++ uses an expanded DyMesh-XL dataset, a redesigned DyMeshVAE-Flex with power-law topology-aware attention, and a rectified-flow generator supporting variable-length sequences for text-driven mesh animation.

In practice

• Generate diverse 4D content from text prompts.
• Animate arbitrary 3D meshes efficiently.
• Create longer, high-fidelity mesh animations.

Topics

• 4D Foundation Model
• Text-Driven Animation
• 3D Mesh Animation
• DyMesh-XL Dataset
• DyMeshVAE-Flex

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.