TextMesh4D: Text-to-4D Mesh Generation via Jacobian Deformation Field

TL;DR

TextMesh4D introduces a novel framework for generating dynamic 4D meshes directly from text, overcoming topological and semantic challenges with innovative deformation and regularization techniques, achieving high fidelity and temporal consistency.

Contribution

The paper proposes the Jacobian Deformation Field and Local-Global Semantic Regularizer, enabling flexible, topologically unconstrained, and semantically coherent text-to-4D mesh generation.

Findings

State-of-the-art temporal consistency and fidelity.

Effective mesh deformation without topological constraints.

High-quality results with a single GPU.

Abstract

Dynamic 3D (4D) content generation, particularly text-to-4D, remains a challenging and under-explored problem due to its inherent spatiotemporal complexity. Existing text-to-4D methods typically avoid direct mesh generation due to inherent topological constraints, favoring alternative representations like NeRFs or 3DGS. However, these non-mesh approaches, suffer from insufficient geometric fidelity, temporal artifacts, and limited compatibility with modern computer graphics (CG) pipelines. In contrast, directly generating dynamic meshes faces two key challenges: i) deformation inflexibility, as traditional vertex-based optimization is constrained by meshes' explicitly encoded topology, and ii) semantic inconsistency, arising from stochastic noise in distilled priors. In this paper, we introduce TextMesh4D, a pioneering framework for text-to-4D mesh generation that directly addresses these challenges. TextMesh4D features two core innovations: 1) the Jacobian Deformation Field (JDF), which shifts the deformation unit from vertices to faces, using per-face Jacobians to model flexible transformations free from topological constraints. 2) the Local-Global Semantic Regularizer (LGSR), which leverages the mesh's innate geometric properties to enforce semantic coherence both locally and globally across frames. Extensive experiments demonstrate that TextMesh4D achieves state-of-the-art performance in temporal consistency, structural fidelity, and visual realism, while requiring only a single 24GB GPU. Our work establishes a new benchmark for efficient and high-quality text-to-4D mesh generation. The code will be released to facilitate future research.