3D Content Training with Timestep Image Reuse for Faster Gradients
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Solution Overview
Problem
Content generation using neural networks, particularly for 3D elements, is computationally expensive and time-consuming due to the repeated generation and rendering of assets during training, leading to increased costs and long development times.
Innovation Solution
Reusing a rendered image multiple times at different timesteps to generate gradients, which are then averaged to update the 3D model, reducing the need for repeated asset generation and computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If neural networks repeatedly generate and render 3D content elements during training, then model training completeness is improved, but computational cost and training time increase significantly
Solution Approach 1:
The patent pre-renders reference images at different timesteps before training begins. These pre-rendered images are then reused multiple times during gradient computation across different training iterations, eliminating the need to regenerate 3D content elements repeatedly. This preliminary preparation significantly reduces training time while maintaining model training completeness.
Solution Approach 2:
The patent creates copies of pre-rendered reference images and reuses them across multiple training iterations and different timesteps. Instead of generating new 3D content elements each time a gradient is needed, the system copies and reuses the pre-rendered images, dramatically reducing computational cost and training time while preserving training effectiveness.
2Measurement precision
If neural networks generate new 3D content elements for each training iteration, then model learning accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The system pre-computes and stores reference images at various timesteps before training starts. During training, these pre-computed references are reused for gradient calculation across multiple iterations, eliminating repeated expensive 3D rendering operations. This maintains learning accuracy through proper gradient computation while drastically reducing computational resource consumption.
Solution Approach 2:
The patent discards the traditional approach of generating new 3D content elements for each training iteration and recovers computational efficiency by reusing pre-rendered reference images. The reference images are discarded from the generation pipeline but recovered as static references for gradient computation, reducing energy consumption while maintaining model learning accuracy.
3Measurement precision
If multiple gradients are computed by regenerating 3D assets, then gradient accuracy is improved, but development cost increases
Solution Approach 1:
The patent prepares reference images at different timesteps in advance, before training begins. These pre-prepared images enable multiple gradient computations to be performed using the same reference data, maintaining gradient accuracy through proper timestep-based gradient calculation while avoiding repeated expensive asset generation that would increase computational resource usage.
Data Source
AI summary
Approaches presented herein provide systems and methods to reuse a rendered image for noising and denoising steps used for training one or more content generation systems. The reused rendered image may reduce computationally expensive processes, such as content generation and rendering, and enable multiple gradients to be compared using a common image that may be noised and then processed by one or more diffusion models to compute a gradient. The gradients may be combined and used to retrain the model, providing more training data with less variance between generating and rendering steps.