Auto-Regressive Mesh Auto-Encoder for Variable-Length 3D Generation
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Solution Overview
Problem
Existing 3D mesh generation methods face challenges in generating complex objects with high-resolution surfaces and varying face counts, often requiring inefficient face tokenization and lossy compression, and struggle with generalization beyond the training domain.
Innovation Solution
An auto-regressive auto-encoder compresses variable-length triangular meshes into fixed-length latent codes, using a tokenizer that maximizes edge sharing and a decoder to generate variable-length mesh token sequences, enabling efficient training and generation of artistic meshes with controlled face counts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If auto-regressive models are trained with difficult input modalities to improve generalization, then model generalization improves, but training difficulty and complexity increase
Solution Approach 1:
The patent segments the mesh representation into multiple input modalities (vertex coordinates, face indices, edge information) that can be processed separately and combined. This segmentation allows the model to learn from different aspects of mesh structure independently, improving generalization while managing training complexity through modular processing
Solution Approach 2:
The patent introduces intermediate representations and preprocessing steps that transform difficult input modalities into more manageable forms. These intermediaries facilitate the training process by bridging the gap between complex raw inputs and the model's internal processing, enabling better generalization without overwhelming training complexity
2Productivity
If face tokenization algorithms are used to generate meshes, then mesh generation is achieved, but the number of faces is limited to fewer than 1,600 due to inefficient algorithms
Solution Approach 1:
The patent employs dynamic face tokenization algorithms that adapt the tokenization process based on the complexity and resolution requirements of the target mesh. This dynamic approach allows the system to generate meshes with significantly more than 1,600 faces by adjusting the tokenization strategy during generation, thereby improving both productivity and manufacturing precision
Solution Approach 2:
The patent changes key parameters of the tokenization algorithm, including resolution levels, face distribution strategies, and compression ratios. By optimizing these parameters, the system overcomes the 1,600-face limitation and generates high-resolution meshes with appropriate face counts for complex objects
3Loss of substance
If mesh vertices are quantized to a discrete grid of 128³ resolution, then compression is achieved, but significant accuracy loss and unsmooth surfaces occur
Solution Approach 1:
The patent moves beyond simple 128³ grid quantization by introducing additional dimensional information and multi-resolution representations. This approach preserves surface accuracy by capturing geometric details that would be lost in uniform quantization, while still achieving compression through hierarchical and adaptive encoding strategies
Solution Approach 2:
The patent applies local quality enhancement by using variable resolution quantization that adapts to local geometric features. Regions requiring higher precision (such as curved surfaces or detailed areas) receive finer quantization, while flat regions use coarser quantization. This local adaptation maintains surface smoothness and accuracy while optimizing overall compression
Data Source
AI summary
Automatic 3D content generation, particularly the generation of polygonal meshes, is useful for development of digital gaming, virtual reality, and filmmaking. Generative models in particular make 3D asset creation more accessible to non-experts. Some existing approaches rely on continuous 3D representations which lose the discrete face indices in triangular meshes during conversion and consequently require post-processing to extract triangular meshes which will then differ significantly from artist-created ones. More recently, attempts have been made to tokenize meshes into 1D sequences and leverage auto-regressive models for direct mesh generation, which can preserve the topology information and generate artistic meshes, but these methods are inefficient, result in accuracy loss, and cannot generalize beyond the training domain. The present disclosure provides an auto-regressive auto-encoder configured for artistic mesh generation, which can compress variable-length triangular meshes into fixed-length latent codes to enable training latent diffusion models conditioned on different modalities for improved generalization.