Diffusion Model Weights for Dynamic Scene Reconstruction
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Solution Overview
Problem
Neural radiance fields (NeRF) are computationally expensive and require substantial memory resources, making them impractical for reconstructing dynamic scenes, especially due to the need for training on each frame and handling changes in external illumination.
Innovation Solution
A computer-implemented method using diffusion models, where a first artificial neural network generates weights for a diffusion model, which is then used to generate image sequences by configuring a second artificial neural network with these weights and applying them to a set of data derived from image frames containing scene information, thereby reducing the computational and memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If NeRF models are used to generate photorealistic views from novel viewpoints, then image quality and scene reconstruction accuracy are improved, but computational cost and memory requirements increase substantially
Solution Approach 1:
The patent creates a simplified copy of the scene using point cloud representations and neural radiance fields that capture essential scene characteristics without requiring full high-resolution 3D modeling. This allows novel view synthesis with reduced computational overhead while maintaining acceptable visual quality.
Solution Approach 2:
The scene is segmented into discrete points or point clouds rather than continuous volumetric representations. This segmentation reduces the data density and computational complexity required for rendering while preserving the essential geometric and radiance information needed for photorealistic view generation.
2Adaptability or versatility
If NeRF models are trained on each frame to reconstruct dynamic scenes, then scene dynamics are captured accurately, but training time and memory resources become prohibitive
Solution Approach 1:
The patent performs preliminary processing to extract point cloud representations and key radiance features from input frames before actual model training. This preprocessing step organizes data in a manner that accelerates subsequent training convergence and reduces the computational burden of training on dynamic scenes.
Solution Approach 2:
The patent changes the representation parameters from dense volumetric grids to sparse point cloud structures. This parameter change fundamentally reduces the state space that needs to be modeled, enabling faster training on dynamic scenes while maintaining reconstruction accuracy.
3Measurement precision
If high-resolution radiance information is stored for 3D space, then rendering quality is improved, but data storage requirements become excessive
Solution Approach 1:
The patent uses sparse point cloud representations analogous to porous structures, where radiance information is stored only at discrete point locations rather than throughout the entire volumetric space. This creates a porous data structure that maintains high resolution at sampled points while dramatically reducing overall storage requirements.
Solution Approach 2:
Instead of storing radiance in continuous volumetric space and sampling as needed, the patent inverts the approach by storing radiance at discrete points and interpolating or rendering from these points. This inversion fundamentally changes the data storage paradigm from dense to sparse.
Data Source
AI summary
A computer-implemented method for generating image sequences includes receiving, at a computing device, values of a set of weights for a diffusion model. The weights are generated by training a first artificial neural network using training frames of training image data in combination with a first set of data derived from the frames of training image data. The values of the set of weights are adjusted during the training. A second artificial neural network present on the computing uses the values of the set of weights and receives a second set of data derived from frames of image data containing at least some scene information present in the training frames of training image data. The second set of data is provided to the second artificial neural network to implement the diffusion model. Images corresponding to the frames of image data are then generated by the second artificial neural network.