Multi-Image Pose-Guided Synthesis for Photorealistic Human Textures
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Solution Overview
Problem
Conventional image synthesis techniques struggle with accurately representing the 3D structure and texture of human bodies, especially when view changes are drastic, due to occlusions and limited visible regions from a single view, leading to poor quality and realism in synthesized human images.
Innovation Solution
A computer system uses a multi-image set and pose data to generate images by combining pose features and appearance features from multiple views, employing machine learning models to warp and fuse images in both UV and pixel spaces, and incorporating a conditional patch loss for improved fidelity and detail.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single-view images are used for image synthesis, then the device complexity is reduced, but the textural quality and photorealism of synthesized images deteriorates due to occlusions and limited visible regions
Solution Approach 1:
The patent transitions from 2D single-view images to 3D multi-view representations by introducing UV space warping and 3D coordinate system transformations. This allows the system to synthesize images from multiple perspectives and combine them to overcome occlusion limitations, thereby improving textural quality without proportionally increasing system complexity.
Solution Approach 2:
The patent introduces intermediate representations including UV coordinate mappings, 3D mesh models, and warping fields as mediators between input images and synthesized output. These intermediaries enable the system to process and fuse multi-view information effectively, achieving high-fidelity synthesis while managing computational complexity through structured intermediate stages.
2Manufacturing precision
If multi-view images are used for image synthesis, then the textural quality and photorealism improves, but the device complexity and processing time increases
Solution Approach 1:
The patent segments the image synthesis process into distinct modules: UV space warping, pixel space warping, feature extraction, and image fusion. Each module handles a specific aspect of the transformation, making the overall complex system more manageable and efficient. This segmentation allows parallel processing of multiple views while maintaining modular architecture.
Solution Approach 2:
The patent transforms images between different coordinate spaces (UV space, pixel space, 3D space) and adjusts warping parameters to optimize the fusion of multi-view images. By changing spatial parameters and transformation variables, the system efficiently integrates information from multiple views without requiring proportional increases in computational resources.
3Measurement precision
If multi-view images are used for image synthesis, then the geometric structure parsing accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary UV space warping and 3D coordinate transformations on input images before the main synthesis process. By pre-processing images to establish accurate geometric mappings and warping fields in advance, the system achieves high geometric structure parsing accuracy while reducing the computational burden during the actual synthesis stage, thereby optimizing processing time.
Data Source
AI summary
Techniques for image generation based on a multi-image set and pose data is described herein. In an example, a computer system generates, by at least using a first machine learning (ML) model, a pose feature based on pose data that indicates a target pose. The computer system generates, by at least using a second ML model, a first appearance feature based on a first image and the pose data. The computer system generates, by at least using the second ML model, a second appearance feature based on a second image and the pose data. The computer system generates a combined appearance feature based on the first appearance feature and the second appearance feature. The computer system generates, by at least using a third ML model, a third image showing an appearance of an element in the target pose based on the pose feature and the combined appearance feature.