3D Object Reconstruction from Video Using Temporal Consistency
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Solution Overview
Problem
Current computer vision techniques face challenges in reconstructing the 3D structure of deformable objects, particularly non-rigid objects like animals, from images and videos captured in naturalistic environments, due to the lack of suitable supervision and generalization issues in constrained domains.
Innovation Solution
A 3D object reconstruction neural network system that learns to predict a dynamic 3D representation of an object from a video by exploiting temporal consistency, using texture, identity shape, and part correspondence invariance constraints, allowing for real-time inference without requiring annotated 3D meshes or camera poses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional 3D reconstruction approaches are used for rigid objects in constrained environments, then 3D annotations can be captured, but the approaches do not generalize well to non-rigid objects in naturalistic environments
Solution Approach 1:
The patent applies dynamics by modeling non-rigid objects as deformable surfaces that change shape over time. The system uses temporal consistency constraints to track how object geometry evolves across video frames, allowing the reconstruction to adapt to dynamic deformations while maintaining reliability through consistent shape priors and smoothness regularization.
2Adaptability or versatility
If 3D annotations are collected without domain limitations, then generalization to non-rigid objects becomes possible, but it remains very difficult due to lack of supervision and constrained environments
Solution Approach 1:
The patent applies self-service by using self-supervised learning where the system learns to reconstruct 3D shapes from unlabeled video data without requiring manual annotations. The temporal consistency of object appearance across frames provides implicit supervision, allowing the model to automatically learn shape priors and deformation patterns from raw video input.
3Adaptability or versatility
If learning-based algorithms are used for 3D reconstruction, then the ability to handle non-rigid objects improves, but the lack of supervision available for training becomes a bottleneck
Solution Approach 1:
The patent applies feedback by using temporal consistency feedback from video sequences. The system leverages the fact that object appearance remains consistent across frames despite deformations, using this temporal feedback to constrain the solution space and guide the learning process without requiring external annotations.
4Measurement precision
If conventional approaches are limited to constrained environments, then 3D annotations can be captured, but real-time inference from videos in the wild becomes difficult
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network on synthetic data with known ground truth 3D annotations. This preliminary training establishes shape priors and reconstruction capabilities that can then be adapted to real-world videos through self-supervised fine-tuning, enabling real-time inference without requiring annotated training data for each specific scenario.
Data Source
AI summary
A three-dimensional (3D) object reconstruction neural network system learns to predict a 3D shape representation of an object from a video that includes the object. The 3D reconstruction technique may be used for content creation, such as generation of 3D characters for games, movies, and 3D printing. When 3D characters are generated from video, the content may also include motion of the character, as predicted based on the video. The 3D object construction technique exploits temporal consistency to reconstruct a dynamic 3D representation of the object from an unlabeled video. Specifically, an object in a video has a consistent shape and consistent texture across multiple frames. Texture, base shape, and part correspondence invariance constraints may be applied to fine-tune the neural network system. The reconstruction technique generalizes well—particularly for non-rigid objects.