Multimodal 3D Pose Embedding for Ambiguous Human Pose Representation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies fail to effectively combine and utilize multiple modalities such as images, 3D poses, and textual descriptions for comprehensive human pose representation, particularly in navigating robots, leading to incomplete or ambiguous representations that hinder human-robot interaction and applications like personalized coaching and virtual avatars.
Innovation Solution
A system and method that uses a combination module with a Transformer architecture to aggregate information from image, 3D pose, and text encodings, generating a rich, semantic-, visual-, and 3D-aware final pose representation by training with unimodal contrastive objectives, allowing for enhanced pose generation and retrieval across modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple modalities (image, 3D pose, text) are combined to generate comprehensive pose representation, then the completeness and accuracy of pose representation is improved, but the system complexity increases
Solution Approach 1:
The system divides the complex multi-modal pose representation task into separate encoding modules for each modality (image encoder, 3D pose encoder, text encoder). Each encoder independently processes its specific input type and generates a pose embedding, which are then combined. This segmentation allows the system to handle multiple modalities without requiring a single complex processing unit, thereby improving pose representation accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The system employs a universal pose embedding space that can represent poses from multiple different modalities (images, 3D poses, text descriptions) in a unified format. The encoders are designed to map diverse input types into this common embedding space, enabling the system to process and combine information from any modality combination. This multi-functional approach allows comprehensive pose representation without requiring separate processing pipelines for each modality pair, thus improving accuracy while controlling complexity.
2Reliability
If separate encoder modules are used for each modality, then the processing of each modality is optimized, but the number of components and system complexity increases
Solution Approach 1:
The system merges the outputs of multiple modality-specific encoders into a unified pose embedding representation. Each encoder (image encoder, 3D pose encoder, text encoder) processes its input independently to ensure optimal modality-specific processing quality, then their embeddings are combined through a combination module that integrates the information into a single comprehensive pose representation. This merging strategy maintains high processing quality for each modality while consolidating the final output, thereby improving reliability without proportionally increasing the number of components.
Data Source
AI summary
A three dimensional (3D) pose determination system includes: a first multilayer perceptron (MLP) module configured to determine an image representation of a pose of the animal in an image; a second MLP module configured to determine a 3D pose representation of a 3D pose of the animal; a third MLP module configured to determine a text pose representation of a textual description of the pose of the animal; and a combination module configured to generate a final 3D pose representation of the pose of the animal based on at least one of the image representation, the 3D pose representation, and the text pose representation.