LN3Diff: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation
(2403.12019)Abstract
The field of neural rendering has witnessed significant progress with advancements in generative models and differentiable rendering techniques. Though 2D diffusion has achieved success, a unified 3D diffusion pipeline remains unsettled. This paper introduces a novel framework called LN3Diff to address this gap and enable fast, high-quality, and generic conditional 3D generation. Our approach harnesses a 3D-aware architecture and variational autoencoder (VAE) to encode the input image into a structured, compact, and 3D latent space. The latent is decoded by a transformer-based decoder into a high-capacity 3D neural field. Through training a diffusion model on this 3D-aware latent space, our method achieves state-of-the-art performance on ShapeNet for 3D generation and demonstrates superior performance in monocular 3D reconstruction and conditional 3D generation across various datasets. Moreover, it surpasses existing 3D diffusion methods in terms of inference speed, requiring no per-instance optimization. Our proposed LN3Diff presents a significant advancement in 3D generative modeling and holds promise for various applications in 3D vision and graphics tasks.
Overview
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SLNFD introduces a scalable framework for high-quality, efficient, and versatile conditional 3D object generation.
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Leverages a variational autoencoder (VAE) and a transformer-based decoder for efficient 3D-aware latent space encoding and data-efficient synthesis.
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Employs latent diffusion learning and a U-Net architecture for denoising, significantly enhancing 3D reconstruction and generation quality.
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Demonstrates superior performance on ShapeNet benchmark, showcasing advancements in 3D object generation and conditional synthesis.
Scalable Latent Neural Fields Diffusion for 3D Object Generation
Introduction
Recent advancements in generative models and differentiable rendering have significantly contributed to the progression of 3D object synthesis. Despite notable achievements in 2D image synthesis through diffusion models, transitioning these successes into a unified 3D diffusion pipeline remains challenging. This paper introduces a novel framework, termed Scalable Latent Neural Fields 3D Diffusion (SLNFD), aimed at overcoming the limitations of existing approaches by enabling efficient, high-quality, and versatile conditional 3D generation.
3D Generation Challenges
The current 3D object generation landscape involves either 2D-lifting methods or feed-forward 3D diffusion models. Both approaches present limitations, including scalability challenges, computational inefficiency, and a lack of support for conditional generation across diverse 3D datasets. The proposed SLNFD framework seeks to address these by leveraging a variational autoencoder (VAE) to encode input images into a lower-dimensional 3D-aware latent space. This space serves as a foundation for a transformer-based decoder that ensures a high-capacity, data-efficient 3D synthesis process.
Framework Overview
Perceptual 3D Latent Compression
At the core of SLNFD is an encoder that compresses images into a 3D-aware latent space, significantly reducing the dimensionality while retaining essential geometric information. The encoder is complemented by a sophisticated decoding mechanism that consists of a transformer architecture, facilitating 3D-aware attention mechanisms, and an upsampling process to achieve high-resolution tri-plane representations. This design not only enhances the quality of 3D reconstruction but also streamlines subsequent diffusion learning phases.
Latent Diffusion and Denoising
The subsequent stage involves latent diffusion learning, where a pre-trained encoder from the compression phase encodes incoming data. This setup allows for efficient utilization of the model for 3D generation. The denoisation process within this stage employs a U-Net architecture tailored for time-dependent operations, ensuring fast and effective variance reduction from noisy data.
Conditioning Mechanisms
A notable strength of SLNFD is its support for conditional generation, facilitated through the injection of conditions (such as text or images encoded using CLIP embeddings) into the latent diffusion model. This feature enables the generation of 3D objects based on descriptive captions or associated images, presenting significant potential for diverse and customized 3D synthesis.
Contributions and Results
The SLNFD model demonstrates superior capability in 3D object generation, offering marked improvements over existing GAN-based and diffusion-based methods. Through empirical evaluations, SLNFD showcases state-of-the-art performance on the ShapeNet benchmark, outperforming competitors in terms of generation quality and inference speed. Additionally, the model proves effective in monocular 3D reconstruction and conditional 3D generation across various datasets, highlighting its versatility and efficiency.
Future Implications
The SLNFD framework introduces a 3D-representation-agnostic approach to constructing high-quality 3D generative models. Its ability to efficiently encode and synthesize 3D objects, combined with support for conditional generation, paves the way for significant advancements in 3D vision and graphics tasks. Future research could extend the framework's application range, explore improvements to its architecture, and investigate its potential in solving complex 3D synthesis challenges.
In conclusion, SLNFD represents a significant step forward in the field of 3D object generation, offering a novel solution that addresses key challenges associated with scalability, efficiency, and versatility. Its contributions not only underscore the potential of diffusion models in 3D generation but also open avenues for further exploration and innovation in this rapidly evolving domain.