FastMesh: Efficient Artistic Mesh Generation via Component Decoupling
This work addresses a computational bottleneck in 3D mesh generation for applications like computer graphics and AI, offering a significant speed-up with incremental improvements in method design.
The paper tackles the inefficiency of autoregressive mesh generation by decoupling vertices and faces, reducing token count to 23% of existing methods and achieving over 8x faster generation speed while improving mesh quality.
Recent mesh generation approaches typically tokenize triangle meshes into sequences of tokens and train autoregressive models to generate these tokens sequentially. Despite substantial progress, such token sequences inevitably reuse vertices multiple times to fully represent manifold meshes, as each vertex is shared by multiple faces. This redundancy leads to excessively long token sequences and inefficient generation processes. In this paper, we propose an efficient framework that generates artistic meshes by treating vertices and faces separately, significantly reducing redundancy. We employ an autoregressive model solely for vertex generation, decreasing the token count to approximately 23\% of that required by the most compact existing tokenizer. Next, we leverage a bidirectional transformer to complete the mesh in a single step by capturing inter-vertex relationships and constructing the adjacency matrix that defines the mesh faces. To further improve the generation quality, we introduce a fidelity enhancer to refine vertex positioning into more natural arrangements and propose a post-processing framework to remove undesirable edge connections. Experimental results show that our method achieves more than 8$\times$ faster speed on mesh generation compared to state-of-the-art approaches, while producing higher mesh quality.