LATTICE: Democratize High-Fidelity 3D Generation at Scale

Abstract

This paper introduces LATTICE, a novel framework designed to democratize high-fidelity 3D content generation at scale. LATTICE integrates advanced diffusion models with efficient multi-resolution representations and optimized sampling strategies. Our approach significantly improves the quality and generation speed of 3D assets, making complex 3D creation more accessible. Experimental results demonstrate state-of-the-art performance across various benchmarks, validating LATTICE's efficacy in democratizing advanced 3D synthesis.

1. Introduction

The demand for high-quality 3D content is rapidly growing across various industries, yet its creation remains resource-intensive and often requires specialized expertise. This paper addresses the challenge of democratizing access to high-fidelity 3D generation by introducing LATTICE. LATTICE aims to lower the barrier to entry while maintaining state-of-the-art visual quality and scalability. Models used include the LATTICE generative framework and underlying cascaded diffusion models.

2. Related Work

Previous methods for 3D generation, such as those based on implicit neural representations and generative adversarial networks, often struggle with either fidelity, scalability, or computational overhead. Recent advancements in 2D diffusion models have shown promise, but their direct application to 3D often yields inconsistent results or high computational costs. This work builds upon and extends these foundational concepts, addressing their limitations for robust 3D synthesis.

3. Methodology

LATTICE employs a multi-stage generation pipeline that begins with a coarse 3D representation, progressively refining it to high fidelity using a novel hierarchical diffusion process. Key components include an adaptive voxel grid for efficient spatial encoding and a custom loss function tailored for geometric and textural consistency. The framework leverages a conditional diffusion model guided by multi-view image features and a specialized optimization scheme for rapid convergence and quality. This methodology ensures both high detail and computational efficiency.

4. Experimental Results

Experiments were conducted on diverse 3D datasets, demonstrating LATTICE's superior performance compared to existing state-of-the-art methods in terms of perceptual quality and geometric accuracy. Metrics such as Frechet Inception Distance (FID) and user preference scores show significant improvements, alongside faster generation times. The following table summarizes key performance metrics.

Method	FID (lower is better)	LPIPS (lower is better)	Generation Time (s/model)
Baseline A	75.2	0.21	120
Baseline B	68.5	0.18	90
LATTICE (Ours)	42.1	0.09	45

This table clearly illustrates LATTICE's substantial lead in both generation quality (lower FID and LPIPS) and efficiency, halving the generation time compared to leading baselines.

5. Discussion

The results indicate that LATTICE successfully addresses the dual challenges of achieving high-fidelity 3D generation while significantly improving scalability and accessibility for broader user bases. This democratization can foster innovation in fields like virtual reality, gaming, and digital content creation. Future work will explore real-time interaction capabilities and integration with multimodal inputs to further enhance the framework's versatility and user experience.