Meshtron: High-Fidelity, Artist-Like 3D Mesh Generation at Scale (2412.09548v1)

Abstract: Meshes are fundamental representations of 3D surfaces. However, creating high-quality meshes is a labor-intensive task that requires significant time and expertise in 3D modeling. While a delicate object often requires over $10^4$ faces to be accurately modeled, recent attempts at generating artist-like meshes are limited to $1.6$K faces and heavy discretization of vertex coordinates. Hence, scaling both the maximum face count and vertex coordinate resolution is crucial to producing high-quality meshes of realistic, complex 3D objects. We present Meshtron, a novel autoregressive mesh generation model able to generate meshes with up to 64K faces at 1024-level coordinate resolution --over an order of magnitude higher face count and $8{\times}$ higher coordinate resolution than current state-of-the-art methods. Meshtron's scalability is driven by four key components: (1) an hourglass neural architecture, (2) truncated sequence training, (3) sliding window inference, (4) a robust sampling strategy that enforces the order of mesh sequences. This results in over $50{\%}$ less training memory, $2.5{\times}$ faster throughput, and better consistency than existing works. Meshtron generates meshes of detailed, complex 3D objects at unprecedented levels of resolution and fidelity, closely resembling those created by professional artists, and opening the door to more realistic generation of detailed 3D assets for animation, gaming, and virtual environments.

• The paper presents an autoregressive model that uses an hourglass architecture to generate high-fidelity 3D meshes with up to 64,000 faces and 1024-level vertex resolution.
• It employs truncated sequence training and sliding window inference, reducing memory usage by over 50% and boosting throughput by 2.5 times compared to traditional methods.
• A robust sampling strategy enforces logical mesh ordering, enabling automated generation of artist-quality 3D assets for animation, gaming, and virtual environments.

Overview of the Meshtron Paper

The paper "Meshtron: High-Fidelity, Artist-Like 3D Mesh Generation at Scale," presents Meshtron, an innovative autoregressive model designed for generating high-resolution 3D meshes. Traditional approaches to 3D mesh generation have been constrained by face count and vertex coordinate resolutions, often resulting in subpar mesh quality compared to those crafted by artists. Meshtron addresses these limitations, offering a groundbreaking shift in generating artist-like meshes with a significant increase in both face count and coordinate resolution.

Contributions

Meshtron introduces several key innovations:

1. Architectural Advancement: By utilizing an hourglass neural architecture, Meshtron captures the hierarchical nature of mesh sequences. This architecture effectively processes different abstraction levels of mesh sequences, aligning with the structure of vertex coordinates, vertices, and faces. The hourglass design allows efficient allocation of computational resources, enhancing training and inference efficiency.
2. Training and Inference Mechanisms: The paper highlights the adoption of truncated sequence training and sliding window inference. This mechanism alleviates the quadratic costs associated with traditional Transformers, markedly reducing memory requirements by over 50% and improving throughput by 2.5 times compared to existing techniques without compromising performance.
3. Robust Sampling Strategy: Meshtron innovates in enforcing order within generated mesh sequences through a robust sampling strategy. This method ensures that generated meshes maintain logical consistency, thereby closely mirroring artist-created meshes.

Key Results

Meshtron can generate 3D meshes with up to 64,000 faces at a 1024-level vertex coordinate resolution, surpassing the state-of-the-art by a wide margin—over an order of magnitude in face count and 8 times higher coordinate resolution. The employed strategies not only enhance the model's scalability but also contribute to generating more detailed and realistic 3D assets for diverse fields such as animation, gaming, and virtual environments.

Implications and Future Directions

Practical Implications: Meshtron enables the automatic creation of high-quality 3D assets, potentially reducing the labor-intensive effort traditionally required in modeling such assets. Consequently, industries relying heavily on 3D assets, such as film, gaming, and design, could significantly benefit from this automation, enhancing both productivity and creative possibilities.

Theoretical Implications: The hourglass architecture introduced for mesh generation could inspire further research to explore its potential in other sequence modeling tasks, benefiting from its hierarchical processing capability and efficiency in handling long sequences.

Speculation on AI Developments: As Meshtron demonstrates, the integration of autoregressive principles with well-designed neural architectures holds promise for advancing generative models. Future research may focus on refining these strategies to support even more complex and intricate 3D representations, potentially paving the way for AI systems capable of generating assets indistinguishable from those crafted by expert human artists.

Meshtron represents a notable advancement in 3D mesh generation, setting a new benchmark for high-quality and scalable mesh creation. As the field progresses, extending these methodologies could lead to unprecedented developments in both theoretical and practical domains of artificial intelligence.