Deformable 3D Model Reconstruction From In-the-Wild Images
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Solution Overview
Problem
Current state-of-the-art methods for monocular 3D reconstruction of deformable objects, such as hands, struggle to generalize to non-laboratory environments and rely on sparse keypoint estimation, limiting their effectiveness in real-world applications.
Innovation Solution
A neural network architecture comprising an encoder and a decoder model is used to generate three-dimensional models from two-dimensional images, trained using a direct 3D object mesh reconstruction loss, with a training dataset generated from videos of deformable objects, particularly hands, to improve pose estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current state-of-the-art methods are used for monocular 3D reconstruction, then the method can generate 3D models, but the method does not generalize well to non-laboratory environments and relies on sparse keypoint estimation
Solution Approach 1:
The patent uses a neural network to learn from labeled 3D data and create a model that can infer 3D representations from 2D images. The neural network copies the relationships between 2D images and 3D models during training, enabling it to generate accurate 3D reconstructions without requiring sparse keypoint annotations in the input images.
Solution Approach 2:
The patent transforms the approach from using sparse keypoint parameters to using dense pixel-based representations. The neural network processes entire 2D images and outputs comprehensive 3D model parameters, changing the parameter representation from sparse keypoints to full 3D mesh or point cloud representations, thereby improving both generalization and precision.
2Measurement precision
If sparse keypoint estimation methods are used, then the method can work with limited input data, but the pose estimation accuracy is limited
Solution Approach 1:
The patent segments the 3D model into a base mesh and deformation parameters, allowing it to capture detailed pose information through a combination of rigid structure and flexible deformation. This segmentation enables the model to recover accurate pose information that would be lost in sparse keypoint methods by distributing information across the entire mesh structure.
Solution Approach 2:
The patent transitions from 2D sparse keypoint data to 3D full-surface representation. By adding the third dimension and using neural networks to process 2D image data into 3D model parameters, the system recovers pose information that exists in the full 3D space, eliminating the information loss inherent in projecting 3D poses to 2D keypoints.
Data Source
AI summary
This specification discloses methods and systems for generating three-dimensional models of deformable objects from two-dimensional images. According to one aspect of this disclosure, there is described a computer implemented method for generating a three dimensional model of deformable object from a two-dimensional image. The method comprises: receiving, as input to an embedding neural network, the two-dimensional image, wherein the two dimensional image comprises an image of an object; generating, using the embedding neural network, an embedded representation of a two-dimensional image; inputting the embedded representation into a learned decoder model; and generating, using the learned decoder model, parameters of the three dimensional model of the object from the embedded representation.