4D Dynamic Scene Reconstruction From Video With Neural Models
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Solution Overview
Problem
Existing machine learning models lack realism and quality in reconstructing and synthesizing dynamic scenes from video data, particularly when data is limited, such as from a monocular camera, leading to challenges in generating accurate novel views.
Innovation Solution
A system using neural networks, including featurizers and transformers, generates a 4D representation of scenes from 2D video data, utilizing pre-trained models like latent diffusion models and depth models, and performs volume rendering to enhance accuracy and realism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional optimization-based approaches are used for scene reconstruction, then the system complexity is reduced, but the realism and quality of the generated 4D representation deteriorate
Solution Approach 1:
The patent replaces conventional optimization-based mechanical/mathematical systems with neural network-based machine learning models. Specifically, it uses a featurizer neural network to extract features from video frames and a transformer neural network to generate the 4D representation, substituting traditional optimization algorithms with learned representations that capture scene dynamics more effectively.
Solution Approach 2:
The patent transforms the reconstruction problem by changing the parameter representation from direct optimization of scene parameters to learning latent features and transformations. The system learns optimal feature representations and transformation parameters through training on video data, allowing high-quality reconstruction without explicit optimization during runtime.
2Manufacturing precision
If more video data is collected to improve reconstruction accuracy, then the manufacturing precision improves, but the loss of time and data processing requirements increase
Solution Approach 1:
The patent performs preliminary action by pre-training the neural network models on large video datasets before actual reconstruction. The featurizer and transformer networks are trained in advance to learn effective feature representations and scene dynamics, so that during actual use, the system can quickly process new video data without requiring extensive computation or time.
Solution Approach 2:
The patent creates a learned copy of scene representations in the form of a 4D content model that captures essential scene properties. Instead of processing all raw video data repeatedly, the system learns compact feature representations and transformation models that can be efficiently applied to generate novel views, reducing processing time while maintaining accuracy.
3Device complexity
If monocular camera data is used to reduce sensor complexity, then the device complexity decreases, but the quantity and quality of available data for reconstruction decreases
Solution Approach 1:
The patent compensates for monocular limitations by introducing temporal and latent feature dimensions. It processes video sequences (adding time dimension) and extracts rich feature representations through the featurizer network, transforming limited 2D spatial information into comprehensive 4D scene understanding by leveraging temporal dynamics and learned features across multiple frames.
Solution Approach 2:
The patent applies asymmetric processing to monocular data by using different neural network components for different aspects of scene understanding. The featurizer extracts various types of features (spatial, temporal, semantic) asymmetrically from the single camera input, and the transformer applies different transformation operations to reconstruct novel views, effectively compensating for the symmetric limitation of monocular sensing.
Data Source
AI summary
In various examples, systems and methods are disclosed relating to reconstruction and synthesis of dynamic scenes from video, such as to generate a four-dimensional (4D) representation of one or more scenes based on one or more videos (e.g., two-dimensional (2D) videos) of the one or more scenes. A system may determine, using a neural network and based on a three-dimensional (3D) representation of one or more scenes, a 4D representation of the one or more scenes, the 3D representation generated by a featurizer using a plurality of first image frames from video data of the one or more scenes. The system may determine, from the 4D representation, a target image having a target pose and a target time.