Conservative Asset Partitioning for Rendering Efficiency
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Solution Overview
Problem
Traditional techniques for rendering computer-generated animation images using dependency graphs require loading all asset information before evaluating the precise subset of assets needed, leading to unnecessary resource consumption and inefficient rendering processes.
Innovation Solution
The system evaluates dependency graphs by using partially culled and potentially over-inclusive sets of assets, where predicate tests are executed based on available asset attributes, allowing for reduced memory loading and resource usage, and passing subsets of assets through the render setup graph to compute a rendered image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all asset information is loaded into memory before evaluating the dependency graph, then the precise subset of assets can be identified, but unnecessary assets are loaded consuming excessive computer resources
Solution Approach 1:
The system performs preliminary evaluation of the dependency graph using only asset attributes (without loading full asset information) to identify a candidate subset of assets. This preliminary action allows the system to determine which assets are likely needed before consuming substantial memory resources, thereby resolving the contradiction between identification precision and resource consumption.
Solution Approach 2:
The asset information is segmented into two parts: asset attributes (evaluated in the first pass) and full asset information (loaded selectively). This segmentation allows the dependency graph to be partially evaluated with minimal memory usage, identifying a candidate asset subset that can then be refined with full information only for necessary assets.
2Quantity of substance
If a subset of assets is used for rendering, then resource consumption is reduced, but the precise necessary assets cannot be identified without loading all asset information
Solution Approach 1:
The system performs a preliminary pass through the dependency graph using only asset attributes to identify a candidate subset of assets. This preliminary action determines which assets warrant full information loading, thereby reducing memory consumption while ensuring that necessary assets are not missed. The preliminary evaluation preserves sufficient information to make accurate asset selection decisions.
3Manufacturing precision
If traditional techniques are used to evaluate the dependency graph, then accurate asset selection is achieved, but rendering time increases due to loading all assets
Solution Approach 1:
The system performs a preliminary evaluation of the dependency graph using asset attributes before loading full asset information. This two-pass approach maintains accurate asset selection by first identifying candidate assets with minimal data, then loading full information only for the identified subset, thereby preserving selection accuracy while significantly reducing rendering time.
Solution Approach 2:
The rendering process is segmented into two distinct phases: a preliminary evaluation phase using asset attributes to identify candidate assets, and a final rendering phase using full asset information only for the identified subset. This segmentation eliminates the need to load all assets before evaluation, maintaining accuracy while reducing time loss.
Data Source
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AI summary
A system for partitioning a set of assets, where each asset represents a computer-generated object associated with a computer-generated scene. A dependency graph comprising a plurality of interconnected nodes including an organizer node is accessed. The set of assets identified by an input of a predicate test of the organizer node are accessed. It is determined if the at least one predicate test can be evaluated using the set of assets. If the at least one predicate test can be evaluated, one or more partition assets are identified and passed to a next node. If the at least one predicate test cannot be evaluated, a conservative set of assets is identified and passed to the next node, wherein the conservative set of assets is the same set of assets identified by the input of the predicate test.