First-Person 3D Pose Prediction with Skeletal-Tree Propagation
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Solution Overview
Problem
Conventional methods for predicting a user's 3D pose from a first-person viewpoint suffer from significant information loss due to the compression of probabilistic heatmaps, leading to inaccurate predictions, especially for body parts hidden by others, resulting in lower accuracy compared to third-person camera-based methods.
Innovation Solution
An apparatus and method that utilize a heatmap estimator to generate limb and joint heatmaps, a grid heatmap encoder to extract joint feature vectors, and a propagation network with a skeletal tree hierarchical structure to propagate relational feature vectors, minimizing information loss and enhancing accuracy by sharing features without down-sampling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If heatmaps are compressed at once by using a CNN-based encoder structure, then the encoding process is simplified and faster, but information loss occurs leading to lower pose prediction accuracy
Solution Approach 1:
The encoder processes heatmaps in a segmented manner rather than compressing all heatmaps at once. The method divides the encoding process into stages: first encoding visible body part heatmaps, then progressively encoding hidden body part heatmaps using propagation networks. This segmentation preserves information by processing different regions with appropriate detail levels rather than uniform compression.
Solution Approach 2:
The method performs preliminary encoding of visible body parts before encoding hidden body parts. By first establishing the pose of visible body parts and then propagating this information to infer hidden body parts, the system preserves critical probabilistic information in advance before any compression occurs, preventing information loss that would occur with simultaneous compression.
2Loss of time
If decoding is performed at once from compressed information, then the processing time is reduced, but pose prediction accuracy decreases due to information loss
Solution Approach 1:
The decoding process is segmented into multiple stages corresponding to different body parts. Visible body parts are decoded first with high accuracy, then hidden body parts are decoded subsequently using propagation from visible parts. This staged approach maintains precision by ensuring each body part is decoded with sufficient information available, rather than attempting simultaneous decoding from insufficiently compressed data.
Solution Approach 2:
The system performs preliminary decoding of visible body parts to establish accurate reference positions before decoding hidden body parts. This preliminary action ensures that the foundation for pose prediction is solid and accurate, allowing subsequent decoding of hidden parts to build upon this accurate foundation rather than attempting all decodings simultaneously from compressed data.
3Device complexity
If conventional encoding methods are used, then the system complexity is reduced, but body parts hidden from first-person viewpoint are predicted inaccurately
Solution Approach 1:
Propagation networks serve as intermediary components between visible and hidden body parts. These networks transfer pose information from visible body parts to infer the positions of hidden body parts, acting as a mediator that bridges the information gap created by occlusion. This intermediary approach maintains reasonable system complexity while significantly improving accuracy for hidden body parts.
Solution Approach 2:
The encoder applies different processing qualities to different body parts based on their visibility. Visible body parts receive full encoding detail, while hidden body parts receive propagated information from visible parts. This local quality differentiation optimizes the balance between complexity and accuracy by applying appropriate processing levels to each region rather than using a uniform complex encoding for all parts.
4Ease of manufacture
If probabilistic information is compressed uniformly, then the encoding is simpler, but the accuracy for both visible and hidden body parts deteriorates
Solution Approach 1:
The encoding process applies different compression levels and processing qualities to different body parts based on their visibility and importance. Visible body parts are encoded with higher fidelity to preserve their probabilistic information, while hidden body parts use propagated information from visible parts. This local quality approach maintains overall simplicity while improving accuracy by concentrating encoding resources where they are most needed.
Data Source
AI summary
Described herein are an apparatus and method for predicting a three-dimensional (3D) pose. The apparatus for predicting a 3D pose includes: an input/output interface configured to receive a plurality of pieces of image data obtained by observing a user's body parts from a first-person viewpoint and output the results of computation processing of the image data; memory configured to store a program for performing a method of predicting a 3D pose; and a controller configured to predict the user's 3D pose based on the image data received through the input/output interface by executing the program. The control unit generates the plurality of pieces of image data as limb heatmaps and joint heatmaps, extracts a joint feature vector, outputs a propagation feature vector by propagating a relational feature vector between neighboring joints, and predicts the user's 3D pose based on the propagation feature vector and the joint feature vector.