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arxiv: 2604.26917 · v1 · submitted 2026-04-29 · 💻 cs.CV

Recognition: unknown

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

Zijie Wu , Chaohui Yu , Fan Wang , Xiang Bai
Authors on Pith no claims yet

Pith reviewed 2026-05-07 10:45 UTC · model grok-4.3

classification 💻 cs.CV
keywords 4D mesh animationtext-driven generation3D meshvariational autoencoderrectified flowdynamic datasettopology-aware attention
0
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The pith

Enlarging the training set to 300K identities and redesigning the variational autoencoder lets a feed-forward model turn text prompts into coherent animated 3D meshes in seconds.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to demonstrate that mining more dynamic 3D content, adding topology-aware attention and normal-vector features to the autoencoder, and enabling variable-length sequence handling together produce a practical text-to-4D system for arbitrary input meshes. A reader would care because existing methods struggle with data scarcity and modeling the joint space-time distribution of mesh vertices, making high-quality animation slow or low-fidelity. The upgrades target both the data bottleneck and the reconstruction artifacts that previously limited quality. Experiments report that the resulting animations stay semantically faithful to the prompt and temporally smooth across diverse objects and motion lengths.

Core claim

AnimateAnyMesh++ is a feed-forward framework that, after expanding DyMesh-XL to 300K identities mined from Objaverse-XL, redesigning DyMeshVAE-Flex with power-law topology-aware attention and vertex-normal enhanced features, and adding variable-length training to both the VAE and the rectified-flow generator, produces semantically accurate and temporally coherent mesh animations from text prompts within seconds.

What carries the argument

DyMeshVAE-Flex, the variational autoencoder upgraded with power-law topology-aware attention and vertex-normal features that encodes and reconstructs variable-length mesh trajectories.

Load-bearing premise

That scaling the dynamic dataset and the new attention and feature choices in the autoencoder produce real gains in trajectory accuracy and artifact reduction on meshes never seen during training.

What would settle it

A side-by-side comparison on a held-out collection of in-the-wild meshes with novel motions, measuring whether animation coherence and artifact counts remain better than the previous version of the model.

Figures

Figures reproduced from arXiv: 2604.26917 by Chaohui Yu, Fan Wang, Xiang Bai, Zijie Wu.

Figure 1
Figure 1. Figure 1: Illustration of our proposed DyMeshVAE-Flex. A long dynamic mesh sequence D is first segmented into overlapping chunks. The initial vertices V0, static faces F, and relative trajectories VT of each chunk are mapped into a decoupled latent space {V n 0 , Vcn T } via the Encoder, utilizing trajectory decomposition and topology-aware attention. The Decoder then reconstructs the relative trajectories V rec T f… view at source ↗
Figure 2
Figure 2. Figure 2: Demonstration of divergent trajectories for nearby mesh vertices. view at source ↗
Figure 3
Figure 3. Figure 3: The architecture of the Shape-Guided Text-to-Trajectory (SGTT) view at source ↗
Figure 4
Figure 4. Figure 4: Comprehensive overview of the DyMesh-XL dataset. (a) Comparison of animation file formats in DyMesh and DyMesh-XL. (b) Quantitative comparison view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative comparison of text-to-4D generation. Given an initial 3D mesh (Input at view at source ↗
Figure 6
Figure 6. Figure 6: Ablation study on our proposed chunk-based trajectory compression view at source ↗
Figure 7
Figure 7. Figure 7: Ablation study on the contributions of Vertex Normal Injection ( view at source ↗
Figure 8
Figure 8. Figure 8: Ablation study demonstrating the effectiveness of our Time-Dependent view at source ↗
read the original 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 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 3 minor

Summary. The manuscript presents AnimateAnyMesh++, a feed-forward framework for text-driven animation of arbitrary 3D meshes. It expands DyMesh-XL to 300K identities by mining dynamic content from Objaverse-XL, redesigns DyMeshVAE-Flex with power-law topology-aware attention and vertex-normal features, and adds architectural support for variable-length sequences in both the VAE and rectified-flow generator. Extensive experiments are reported to show that the model produces semantically accurate and temporally coherent animations in seconds, outperforming prior methods in quality and efficiency with consistent gains on benchmarks and in-the-wild meshes.

Significance. If the quantitative benchmarks and ablations hold, the work advances 4D foundation models by scaling data diversity and improving architectural flexibility for spatio-temporal modeling. The combination of enlarged training data, topology-aware attention, and variable-length training addresses data scarcity and artifact issues in mesh animation. The planned public release of code, models, and the expanded DyMesh-XL dataset strengthens the contribution by enabling reproducibility and follow-on research.

minor comments (3)
  1. [Abstract] Abstract: the summary of results would be more informative if it included one or two key quantitative metrics (e.g., a specific improvement in trajectory error or user-study score) alongside the qualitative claims of superiority.
  2. The description of power-law topology-aware attention would benefit from an explicit equation or pseudocode block to clarify how the attention weights are computed from the topology.
  3. Ensure that all experimental tables report standard deviations or error bars across multiple runs to support the claim of consistent gains.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive summary, significance assessment, and recommendation of minor revision. No specific major comments were raised in the report, so we have no point-by-point rebuttals to provide at this stage. We will incorporate any minor suggestions during revision and ensure the planned release of code, models, and the expanded DyMesh-XL dataset proceeds upon acceptance.

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The paper describes empirical upgrades to dataset size (DyMesh-XL from 60K to 300K identities), architecture (power-law topology-aware attention and vertex-normal features in DyMeshVAE-Flex), and training (variable-length rectified flow support). Performance claims rest on quantitative benchmarks, ablations, and in-the-wild comparisons rather than any derivation, fitted parameter renamed as prediction, or self-citation chain. No equations, uniqueness theorems, or ansatzes are invoked that reduce outputs to inputs by construction. The work is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no explicit free parameters, axioms, or invented entities can be extracted. The work implicitly relies on standard deep-learning assumptions (e.g., that neural networks can approximate spatio-temporal distributions from mesh data) and numerous fitted weights whose values are not reported here.

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