Abstract
The evolution of 3D generative modeling has been notably propelled by the adoption of 2D diffusion models. Despite this progress, the cumbersome optimization process per se presents a critical hurdle to efficiency. In this paper, we introduce Hash3D, a universal acceleration for 3D generation without model training. Central to Hash3D is the insight that feature-map redundancy is prevalent in images rendered from camera positions and diffusion time-steps in close proximity. By effectively hashing and reusing these feature maps across neighboring timesteps and camera angles, Hash3D substantially prevents redundant calculations, thus accelerating the diffusion model's inference in 3D generation tasks. We achieve this through an adaptive grid-based hashing. Surprisingly, this feature-sharing mechanism not only speed up the generation but also enhances the smoothness and view consistency of the synthesized 3D objects. Our experiments covering 5 text-to-3D and 3 image-to-3D models, demonstrate Hash3D's versatility to speed up optimization, enhancing efficiency by 1.3 to 4 times. Additionally, Hash3D's integration with 3D Gaussian splatting largely speeds up 3D model creation, reducing text-to-3D processing to about 10 minutes and image-to-3D conversion to roughly 30 seconds. The project page is at https://adamdad.github.io/hash3D/.
Overview
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Hash3D introduces a training-free method to speed up 3D generation by reusing feature maps through an adaptive grid-based hashing technique, reducing computational demands.
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The method leverages feature-map redundancy across similar camera angles and diffusion time steps, enhancing the efficiency of diffusion models in 3D generative tasks.
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Experimental results show Hash3D's capability to significantly accelerate 3D model generation tasks without compromising quality, with notable reductions in processing time.
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Future work will explore further optimizations and the application of Hash3D to other generative modeling tasks, broadening its practical applications.
Accelerating 3D Generation with Hash3D: A Training-free Approach
Overview of Hash3D
The latest advancements in 3D generative modeling have significantly benefited from incorporating 2D diffusion models. However, the optimization process remains a major bottleneck due to its time-consuming nature. Addressing this challenge, the paper introduces Hash3D, a novel method that accelerates 3D generation tasks without the necessity for model training. Hash3D capitalizes on the observation that there is a high degree of feature-map redundancy across images rendered from closely positioned camera angles and diffusion time steps. By employing an adaptive grid-based hashing system to reuse these feature maps efficiently, Hash3D reduces redundant computations, thereby speeding up the diffusion model's inference in 3D generative tasks. Remarkably, this feature-sharing mechanism not only expedites the generation process but also contributes to the smoothness and consistency of the synthesized 3D objects.
Key Contributions
- Introduction of Hash3D: The paper presents Hash3D as a versatile, plug-and-play, and training-free acceleration method for various diffusion-based text-to-3D and image-to-3D models.
- Efficient Feature Reuse: By leveraging feature-map redundancy in diffusion models, Hash3D significantly diminishes computational demands across view and time, with speed improvements ranging between $1.3\sim 4\times$.
- Adaptive Grid-based Hashing: Hash3D utilizes an adaptive grid-based hashing technique, dynamically adjusting grid sizes based on the proximity of views and timestamps, to maximize the efficiency of feature reuse.
Technical Details and Implementation
Hash3D's core strategy involves a space-time trade-off executed through a grid-based hash table, which serves to store intermediate features from the diffusion model. This hash table allows for the retrieval of relevant features for new views that are close to previously processed ones, thus avoiding redundant calculations.
- Probing Redundancy in SDS: Upon identifying significant similarity in denoising outputs and feature maps from neighboring camera positions and timesteps, Hash3D proposes a hashing-based solution to exploit this redundancy effectively.
- Grid-based Hashing: The method uses a grid-based hashing function to catalogue and retrieve features based on camera pose and timestep, facilitating fast data access and reducing computational load.
- Adaptive Grid Sizing: Recognizing the variability in optimal grid sizes across different scenarios, Hash3D incorporates a mechanism to adaptively adjust grid sizes for each view, ensuring optimal performance.
Experimental Evaluation
The paper reports extensive testing across five text-to-3D and three image-to-3D models, showcasing Hash3D's capability to accelerate optimization significantly while maintaining, and in some cases slightly improving, the quality of 3D model creation. Notable results include reducing the processing time for text-to-3D tasks to approximately 10 minutes and image-to-3D tasks to about 30 seconds.
Implications and Future Directions
Hash3D's introduction marks a significant stride towards addressing the efficiency bottleneck in 3D generative modeling. By offering a method that accelerates the generative process without sacrificing output quality, this research paves the way for broader practical applications of 3D generation technologies. Future work may explore further optimizations in the hashing mechanism and its potential application to other forms of generative models beyond the studied text-to-3D and image-to-3D cases.
In conclusion, Hash3D exemplifies the potential of leveraging computational redundancies to enhance the performance of generative models. Its adaptive, training-free nature not only streamlines the generative process but also opens new vistas for research and development in the field of 3D modeling.
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