3D Scene Generation Model With 2D and 3D Discriminator Training
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Solution Overview
Problem
Conventional 3D scene generation models, such as Neural Radiance Field (NeRF) networks, suffer from shape distortions and chromatic noise when generating 3D remixes from 2D images, due to the use of either a 2D discriminator trained on small patches leading to weakness or large patches resulting in memorization, and lack of modeling free space in the volume.
Innovation Solution
A scene generation model trained progressively using both a 2D discriminator and a 3D discriminator over a sequence of increasing resolutions, incorporating a stagewise training scheme to generate 3D remix scenes without flickering or spatio-temporal artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a 2D discriminator is trained on small patches, then training complexity is reduced, but the discriminator becomes too weak to effectively guide scene generation
Solution Approach 1:
The training process is segmented into multiple stages with increasing patch sizes. Initially, small patches are used to reduce training complexity, then progressively larger patches are introduced to enhance discriminator effectiveness without overwhelming the training system at once.
Solution Approach 2:
The discriminator is first trained on small patches to establish a baseline capability, then progressively refined with larger patches. This preliminary training on simpler data prepares the discriminator for more complex discrimination tasks in subsequent training stages.
2Measurement precision
If a 2D discriminator is trained on large patches, then discrimination accuracy improves, but the model memorizes training data instead of learning general patterns
Solution Approach 1:
The training alternates between periods using smaller patches for generalization and larger patches for accuracy refinement. This periodic variation prevents memorization by regularly resetting the learning scale, allowing the discriminator to maintain both accuracy and adaptability.
Solution Approach 2:
The patch size parameter is dynamically adjusted during training. By changing this parameter across different training stages, the system balances between learning general patterns with small patches and achieving high discrimination accuracy with larger patches, preventing memorization.
3Stability of the object's composition
If only 2D discriminators are used, then training stability is improved, but shape distortions and chromatic noise occur in generated 3D scenes
Solution Approach 1:
The system merges 2D and 3D discriminators into a unified training framework. The 2D discriminator maintains training stability while the 3D discriminator component addresses shape distortions and chromatic noise by providing volumetric feedback, combining the advantages of both approaches.
Solution Approach 2:
The solution transitions from purely 2D discrimination to incorporating 3D discrimination. By adding the third dimension to the discriminator's operation, the system eliminates 3D artifacts while maintaining the stability provided by the 2D foundation through multi-dimensional discriminative guidance.
4Productivity
If conventional scene generation models are used, then 3D remix generation is achieved, but shape distortions and chromatic noise defects occur
Solution Approach 1:
The system implements feedback loops where both 2D and 3D discriminators evaluate generated scenes and provide gradient feedback to the generator. This feedback mechanism continuously refines the 3D remix generation process, reducing shape distortions and chromatic noise while maintaining productivity.
Solution Approach 2:
The generation model uses a composite loss function combining multiple discrimination objectives (2D photometric loss, 3D volumetric loss, shape consistency loss). This composite approach integrates multiple quality constraints into the generation process, eliminating defects while preserving efficient 3D remix generation.
Data Source
AI summary
A scene modeling system accesses a set of input two-dimensional (2D) images of a three-dimensional (3D) environment, wherein the input 2D images captured from a plurality of camera orientations. The environment includes first content. The scene modeling system applies a scene generation model to the set of input 2D images to generate a 3D remix scene. Applying the scene generation model includes configuring the scene generation model using at least a 2D discriminator and a 3D discriminator. Applying the scene generation model includes transmitting, for display via a user interface, the 3D remix scene. The 3D remix scene includes second content that is different from the first content.