3D Motion Effect from 2D Image via Neural Depth Estimation
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Solution Overview
Problem
Current methods for generating 3D motion effects from 2D images are inefficient and require sophisticated editing skills, often relying on manual inputs for depth map creation and intermediate view synthesis, which can be time-consuming and fail to handle general image types effectively.
Innovation Solution
A method that generates a 3D motion effect by creating a depth map, identifying a camera path, generating extremal views, constructing a global point cloud by inpainting occlusion gaps, and synthesizing intermediate views to combine into a 3D motion effect, utilizing a processor-based system with neural networks for automated depth estimation and view synthesis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual foreground segmentation and simplified mesh representation are used to create 3D motion effects, then the editing process becomes controllable and cinematic effects can be achieved, but the process becomes time-consuming and requires sophisticated editing skills
Solution Approach 1:
The system performs automatic foreground segmentation and depth map generation without requiring manual user intervention. The neural network automatically processes the input image to generate the necessary 3D structure and camera motion parameters, eliminating the need for time-consuming manual editing while maintaining high quality results
Solution Approach 2:
The patent replaces manual mechanical editing processes with automated neural network-based image processing. The system uses deep learning models to automatically segment foreground objects, generate depth maps, and synthesize intermediate views, substituting the manual mechanical editing workflow with intelligent automated processing
2Adaptability or versatility
If creative effect synthesis with simplified mesh is used, then 3D illusion can be achieved, but the scene representation becomes too simple to handle general types of images
Solution Approach 1:
The system dynamically adjusts the complexity of scene representation based on the input image characteristics. For simple images, a simplified mesh may suffice, while for complex general image types, the system automatically generates more detailed depth maps and point cloud representations, allowing adaptable handling of various image types without excessive complexity in all cases
Solution Approach 2:
The patent transitions from 2D image representation to 3D point cloud and depth map representations. By generating intermediate views through 3D warping and synthesizing occluded regions in the third dimension, the system achieves realistic 3D motion effects that work for general image types without requiring overly complex manual scene representations
3Measurement precision
If multiple input images from varying viewpoints are used, then accurate depth information can be obtained, but the requirement for multiple inputs increases system complexity and user effort
Solution Approach 1:
The system segments the single input image into foreground and background regions using neural network-based foreground segmentation. By separating different depth layers in the image, the system can generate accurate depth maps and synthesize intermediate views for each segment independently, achieving precise depth information from a single image without requiring multiple viewpoint inputs
Solution Approach 2:
The patent introduces depth maps and point cloud representations as intermediary structures between the single input image and the final 3D motion effect. These intermediaries encode depth information and enable accurate 3D warping and view synthesis, bridging the gap between 2D single-image input and 3D multi-view output without requiring multiple input images
4Ease of operation
If automated neural network processing is used for depth estimation and view synthesis, then processing time is reduced and ease of operation improves, but geometric and semantic distortions may occur
Solution Approach 1:
The system employs feedback mechanisms where the synthesized intermediate views are evaluated and refined. The neural network processes are guided by loss functions that minimize geometric and semantic distortions, and the system iteratively improves the synthesized views by comparing with the original image and adjusting depth maps and warping parameters to maintain geometric accuracy
Data Source
AI summary
Systems and methods are described for generating a three dimensional (3D) effect from a two dimensional (2D) image. The methods may include generating a depth map based on a 2D image, identifying a camera path, generating one or more extremal views based on the 2D image and the camera path, generating a global point cloud by inpainting occlusion gaps in the one or more extremal views, generating one or more intermediate views based on the global point cloud and the camera path, and combining the one or more extremal views and the one or more intermediate views to produce a 3D motion effect.