AI Relighting With Coarse-to-Fine Shading Control
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Solution Overview
Problem
Conventional image generation systems are inefficient, lack user control over lighting, and are not scalable or generalizable, leading to inaccurate and uncontrolled lighting in composite images and videos.
Innovation Solution
A machine learning model generates relighted images using a coarse-to-fine relighting framework, incorporating a lighting estimation model to create a shading map based on user-provided lighting parameters, and an image generation model to produce accurate and user-controllable lighting, with temporal consistency for videos.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image generation systems are used, then image generation can be performed, but lighting control is inaccurate and uncontrolled
Solution Approach 1:
The patent segments the lighting control process into two distinct stages: coarse lighting representation generation and fine-grained relighting. The coarse stage generates an initial lighting map that captures overall lighting conditions, while the fine stage refines this map to achieve precise lighting control. This segmentation resolves the contradiction by breaking down the complex lighting control task into manageable steps, improving accuracy without overwhelming system complexity.
Solution Approach 2:
The patent applies preliminary action by generating a coarse lighting representation before performing fine-grained relighting. This initial lighting map serves as a foundation that guides subsequent detailed lighting adjustments. By establishing the overall lighting structure first, the system achieves better control accuracy while managing complexity through staged processing.
2Manufacturing precision
If fine-grained relighting is implemented, then lighting accuracy is improved, but computational efficiency decreases
Solution Approach 1:
The patent divides relighting into coarse and fine stages, where the coarse stage handles overall lighting computation efficiently, and the fine stage focuses computational resources on refining specific areas. This segmentation maintains lighting precision while improving overall efficiency by avoiding unnecessary fine-grained computation across the entire image.
Solution Approach 2:
The patent applies partial action by focusing fine-grained relighting computation only on regions where precise lighting control is most needed, rather than uniformly applying high-computation processing across the entire image. This approach maintains lighting precision in critical areas while preserving computational efficiency through selective processing.
3Ease of operation
If user control over lighting parameters is enhanced, then lighting controllability is improved, but system complexity increases
Solution Approach 1:
The patent introduces a lighting estimation model as an intermediary that translates user-friendly lighting parameters into the internal representation needed for relighting. This intermediary layer allows users to control lighting through intuitive parameters without directly managing the complex underlying computations, thereby improving ease of operation while managing system complexity through abstraction.
4Stability of the object's composition
If temporal consistency is enforced across video frames, then lighting consistency is improved, but processing time increases
Solution Approach 1:
The patent applies preliminary action by establishing lighting consistency constraints before processing individual video frames. By pre-defining temporal consistency requirements and using the coarse lighting representation to guide frame-by-frame relighting, the system achieves lighting consistency across frames without requiring extensive post-processing or re-computation, thus managing processing time effectively.
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for image generation includes obtaining an object image and a target lighting indicator, generating a shading map based on the object image and the target lighting indicator, and generating a relighted image based on the object image and the shading map. The relighted image depicts an object from the object image with lighting based on the target lighting indicator.