@inproceedings{
chen2024meshxl,
title={Mesh{XL}: Neural Coordinate Field for Generative 3D Foundation Models},
author={Sijin Chen and Xin Chen and Anqi Pang and Xianfang Zeng and Wei Cheng and Yijun Fu and Fukun Yin and Zhibin Wang and Jingyi Yu and Gang Yu and BIN FU and Tao Chen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Gcks157FI3}
}
The polygon mesh representation of 3D data exhibits great flexibility, fast rendering speed, and storage efficiency, which is widely preferred in various applications. However, given its unstructured graph representation, the direct generation of high-fidelity 3D meshes is challenging. Fortunately, with a pre-defined ordering strategy, 3D meshes can be represented as sequences, and the generation process can be seamlessly treated as an auto-regressive problem. In this paper, we validate Neural Coordinate Field (NeurCF), an explicit coordinate representation with implicit neural embeddings, is a simple-yet-effective representation for large-scale sequential mesh modeling. After that, we present MeshXL, a family of generative pre-trained auto-regressive models that addresses 3D mesh generation with modern large language model approaches. Extensive experiments show that MeshXL is able to generate high-quality 3D meshes, and can also serve as foundation models for various down-stream applications.
MeshXL: Neural Coordinate Field for Generative 3D Foundation Models
Sijin Chen Xin Chen Anqi Pang Xianfang Zeng Wei Cheng Yijun Fu Fukun Yin Zhibin Wang Jingyi Yu Gang Yu BIN FU Tao Chen