State Space Model for 3D Human Motion from Continuous Light Signals
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Solution Overview
Problem
Existing techniques for evaluating human motion from video streams struggle to adapt effectively to new frame rates during real-time processing of continuous video frames, impacting applications like pose estimation, mesh recovery, and action recognition.
Innovation Solution
The implementation of a state space model that processes 2D information from continuous time light signals, combined with discretization information about frame rates, to generate accurate 3D information about a user, enhancing adaptability without the need for retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing techniques process video streams at fixed frame rates, then processing is simple, but adaptability to new frame rates requires retraining
Solution Approach 1:
The patent applies parameter changes by using discretization information (frame rate parameters) as inputs to the state space model, allowing the model to adapt to different frame rates through parameter variation rather than retraining. The model receives discretization information about the frame rate and adjusts its processing accordingly, enabling versatility across different video frame rates without changing the model architecture or requiring retraining.
2Measurement precision
If state space model processes continuous time light signals, then accuracy and efficiency improve, but processing complexity increases
Solution Approach 1:
The patent uses discretization information as an intermediary element that bridges the continuous time light signals and the state space model processing. This intermediary parameter allows the model to handle continuous signals efficiently by converting temporal information into discrete representations that can be processed through the state space model, achieving accurate 3D information generation without excessive computational complexity.
3Speed
If real-time processing is performed at high frame rates, then processing speed increases, but computational resources consume more
Solution Approach 1:
The patent applies dynamics by making the processing approach adaptive to the actual frame rate through discretization information. The state space model dynamically adjusts its processing based on the input frame rate, allowing efficient real-time processing at varying speeds without consuming excessive computational resources. The model can operate effectively whether the input is at 30fps, 60fps, or other frame rates, optimizing resource usage according to the actual processing needs.
Data Source
AI summary
Various implementations disclosed herein include devices, systems, and methods that generate 3-dimensional (3D) information related to a user from a continuous time light signal. For example, a process may obtain two-dimensional (2D) information corresponding to a continuous time light signal providing information about a user in a 3D environment. The 2D information may be based on frames comprising images capturing the continuous time light signal at one or more frame rates. The process may further obtain discretization information corresponding to the one or more frame rates. The process may further determine 3D information about the user by inputting the 2D information and the discretization information into a state space model. The state space model may be a continuous time learnable framework for mapping between continuous time 2D scalar inputs and continuous time scalar 3D outputs.