3D Object Re-Lighting Using De-Lighted and Tri-Plane Representations
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Solution Overview
Problem
Existing methods for re-lighting and view synthesis in 3D graphics and photography are cumbersome, require expensive equipment, and lack flexibility, often producing unrealistic shading and inaccurate lighting effects, especially for casual users.
Innovation Solution
A computing system employs machine learning models to generate a de-lighted image, an embedded representation, and a re-lighted representation of a 3D object, combining these with orientation and lighting information to produce a final image, using techniques like GAN inversion and tri-plane representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized hardware is used to record detailed lighting information, then lighting accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces specialized lighting measurement hardware with a machine learning model that processes standard images to extract lighting information. The ML model infers lighting conditions, material properties, and geometry from regular images, eliminating the need for complex photometric stereo cameras or light stage equipment while achieving comparable or superior accuracy.
Solution Approach 2:
The patent creates a virtual copy of the lighting information and material properties through ML-generated representations (embedded representations and tri-plane representations). These digital copies enable lighting transfer and view synthesis without requiring physical hardware to capture and store the original lighting conditions.
2Device complexity
If computational techniques like style transfer are used for re-lighting, then device complexity is reduced, but lighting realism and accuracy deteriorate
Solution Approach 1:
The patent changes the approach from direct image manipulation (style transfer) to parameter-based control. The ML model extracts explicit parameters including lighting direction, intensity, material roughness, and geometry normals, then uses these parameters to control the relighting process. This parameterized approach enables realistic lighting while maintaining system simplicity.
Solution Approach 2:
The patent moves from 2D image space manipulation to 3D volumetric representation using tri-plane representations. By lifting the problem into 3D space with depth information and material properties, the system achieves realistic lighting effects that respect object geometry and material characteristics, overcoming the limitations of 2D style transfer methods.
3Ease of operation
If traditional re-lighting methods are used, then lighting adjustment is achieved, but computational resources and processing time increase
Solution Approach 1:
The patent performs preliminary processing by generating embedded representations and tri-plane representations from input images before lighting transfer. These pre-computed representations capture geometry, material properties, and lighting information in an organized format that enables efficient lighting adjustment. The heavy computational work is done once during representation generation, making subsequent lighting edits computationally inexpensive.
Solution Approach 2:
The patent segments the image processing task into distinct components: geometry extraction, material property estimation, lighting decomposition, and view synthesis. Each component is handled by specialized parts of the ML model and produces intermediate representations that are combined to achieve the final relit image. This segmentation enables efficient computation by processing only relevant information for each lighting adjustment.
Data Source
AI summary
Techniques for volumetric re-lighting of 3D objects are disclosed. In an example method, a computing system receives a first image of a three-dimensional (“3D”) object. The computing system generates a de-lighted image of the 3D object based on the first image. The computing system generates an embedded representation of the 3D object based on the de-lighted image and a first representation of the de-lighted image based on the embedded representation using a first machine learning (“ML”) model. The computing system generates a second representation of the 3D object using a second ML model based on orientation and lighting information and one or more internal states of the first ML model. The computing system generates a third representation of the 3D object by combining the first and second representations. The computing system renders a second image of the 3D object based on the third representation of the 3D object.