Dual3D: Efficient and Consistent Text-to-3D Generation with Dual-mode Multi-view Latent Diffusion
(2405.09874)Abstract
We present Dual3D, a novel text-to-3D generation framework that generates high-quality 3D assets from texts in only $1$ minute.The key component is a dual-mode multi-view latent diffusion model. Given the noisy multi-view latents, the 2D mode can efficiently denoise them with a single latent denoising network, while the 3D mode can generate a tri-plane neural surface for consistent rendering-based denoising. Most modules for both modes are tuned from a pre-trained text-to-image latent diffusion model to circumvent the expensive cost of training from scratch. To overcome the high rendering cost during inference, we propose the dual-mode toggling inference strategy to use only $1/10$ denoising steps with 3D mode, successfully generating a 3D asset in just $10$ seconds without sacrificing quality. The texture of the 3D asset can be further enhanced by our efficient texture refinement process in a short time. Extensive experiments demonstrate that our method delivers state-of-the-art performance while significantly reducing generation time. Our project page is available at https://dual3d.github.io
Overview
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Dual3D proposes a novel method to convert textual descriptions into high-quality 3D models by leveraging dual-mode latent diffusion models (LDMs).
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The approach utilizes pretrained 2D LDMs, a dual-mode inference strategy switching between 2D and 3D modes, and texture refinement processes to achieve efficient and consistent 3D generation.
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The method drastically reduces generation time to approximately 50 seconds while maintaining high quality, showing promising results in user studies and outperforming other methods in metrics like CLIP Similarity, R-Precision, and aesthetic scores.
Understanding Dual3D: Efficient Text-to-3D Generation with Dual-Mode Latent Diffusion
Overview
This article breaks down the paper titled "Dual3D: Efficient and Consistent Text-to-3D Generation with Dual-mode Multi-view Latent Diffusion." This research provides an efficient method to convert textual descriptions into high-quality 3D models using a novel approach leveraging dual-mode latent diffusion models. Below, we'll dive into the details and implications of this fascinating work.
Key Components
Dual-Mode Multi-view Latent Diffusion Model
The core innovation in this paper is the dual-mode multi-view latent diffusion model. Here's how it works:
- Pretrained 2D Latent Diffusion Models (LDMs): The model starts with a pretrained 2D LDM, which is then fine-tuned for 3D purposes. This significantly reduces the training cost and leverages the strengths of already effective 2D models.
- Dual Modes: The model operates in two modes:
- 2D Mode: Efficiently denoises noisy multi-view latents with a single latent denoising network.
- 3D Mode: Generates a tri-plane neural surface for consistent rendering-based denoising. Tri-planes are three images representing different planes of 3D space which help create 3D geometry.
Inference Strategy: The paper proposes a dual-mode toggling inference strategy, which switches between the 2D and 3D modes during inference. By doing this, it achieves high efficiency without compromising on the quality. Specifically, only 1/10th of the denoising steps use the 3D mode, cutting down inference time to just 10 seconds.
Texture Refinement
To enhance the quality of textures in the generated 3D models, a texture refinement process is introduced. This involves:
- Extracting the neural surface into a mesh.
- Converting the texture into a learnable texture map.
- Optimizing this texture map using differentiable rendering and the pretrained 2D LDM.
Numerical Results
The paper boasts impressive results in generating high-quality 3D assets:
- CLIP Similarity & R-Precision: These metrics are used to measure the alignment between the generated 3D assets and the textual descriptions. The method shows strong performance in both CLIP Similarity and R-Precision, indicating that the generated assets are semantically accurate and diverse.
- Aesthetic Score: The generated 3D models are also evaluated for their aesthetic appeal using the LAION Aesthetic Predictor, where the models receive high scores.
- Generation Time: Remarkably, despite the high quality, the method generates models in approximately 50 seconds, a stark contrast to the 3-45 minutes required by other methods like DreamGaussian and MVDream.
User Study
In a user study involving 24 participants, the method was evaluated across various criteria, confirming the subjective quality of the generated 3D assets. The proposed method consistently scored the highest, aligning well with user preferences.
Implications
Practical Implications
This research can significantly impact industries like gaming, robotics, virtual reality (VR), and augmented reality (AR). For example:
- Gaming: Generates diverse and high-quality 3D assets quickly, reducing development time and costs.
- VR/AR: Enhances the realism and detail of virtual objects, improving user experiences.
- Robotics: Provides accurate and detailed 3D models for simulation and interaction in various environments.
Theoretical Implications
On a theoretical level, this research advances the understanding and application of diffusion models in 3D space. By effectively combining multi-view image data and pre-trained 2D LDMs, it opens new avenues for efficient cross-domain model adaptation and multi-modal learning.
Future Directions
While the paper showcases promising results, there are areas for further exploration:
- Handling Complex Text Prompts: The current method struggles with text prompts involving fine-grained or complex concepts. Future research could focus on enhancing the model’s ability to understand and generate intricate multi-object scenes.
- Improving Fine Details: Despite the robust texture refinement process, generating extremely detailed or thin shapes remains challenging. Incorporating more advanced 3D representations, like 3D Gaussian Splatting, could further enhance the quality and realism of the generated assets.
- Real-world Multi-view Data: Integrating real-world multi-view data could improve the models' ability to generate more realistic and contextually rich 3D objects.
Conclusion
Dual3D introduces an innovative and efficient approach to text-to-3D generation, leveraging the strengths of pretrained 2D LDMs and a dual-mode inference strategy. This method sets a new standard in the field, providing high-quality, semantically accurate 3D models while significantly reducing generation time. As the research progresses, it promises to transform various industries by enabling swift and cost-effective creation of realistic 3D assets.
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