Text-Conditioned 3D Pose Generation via Hierarchical PoseScript Dataset
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Solution Overview
Problem
Conventional 3D human pose datasets lack detailed language descriptions, making it difficult to link text and 3D geometry, which hinders applications such as image annotation, semantic searches, and motion generation in digital animation.
Innovation Solution
The PoseScript dataset pairs 3D human poses with complex natural language descriptions using an automatic captioning pipeline that extracts low-level pose information (posecodes) and combines them into higher-level textual descriptions, enabling text-to-pose and pose-to-text retrieval, and text-conditioned pose generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional 3D human pose datasets are used, then the data structure is simple, but detailed language descriptions are lacking
Solution Approach 1:
The patent implements a multi-level hierarchical structure where posecodes (low-level binary indicators) are nested within pose descriptions, which are in turn nested within natural language captions. This nested structure allows detailed language information to be embedded within the 3D pose data framework without flattening the complexity, thereby preserving information while organizing it in a manageable hierarchical manner.
Solution Approach 2:
The patent segments natural language descriptions into structured components including posecodes (binary indicators for body part positions), pose descriptions (structured relationships between body parts), and captions (natural language sentences). This segmentation allows detailed language information to be systematically integrated into the 3D pose dataset while maintaining organizational structure through distinct hierarchical levels.
2Loss of information
If detailed language descriptions are added to 3D pose data, then information completeness improves, but data processing complexity increases
Solution Approach 1:
The patent segments pose information into discrete posecodes (binary indicators for specific body part positions and relationships) that can be independently processed and combined. This segmentation enables systematic processing of detailed pose information through modular operations on individual posecodes rather than handling entire complex descriptions as monolithic units.
Solution Approach 2:
The patent introduces a hierarchical dimension to organize pose information, transitioning from flat 3D coordinates to a multi-level structure with posecodes at the lowest level, pose descriptions in the middle level, and natural language captions at the highest level. This dimensional organization enables efficient processing by allowing operations at different levels of abstraction without requiring complete processing of all detailed information.
3Adaptability or versatility
If natural language to pose retrieval is implemented, then semantic search capability improves, but system complexity increases
Solution Approach 1:
The patent introduces posecodes as intermediary elements that bridge natural language captions and 3D pose data. These binary indicators serve as a mediator that translates semantic information from natural language into structured pose representations, enabling retrieval operations without requiring direct complex mappings between language and geometry, thereby simplifying the retrieval system architecture.
Solution Approach 2:
The patent segments the retrieval process into distinct operational levels: caption-to-posecode matching at the semantic level, posecode-to-pose mapping at the structural level, and pose rendering at the visualization level. This segmentation enables semantic search functionality to be implemented through modular retrieval operations rather than requiring a monolithic complex system.
Data Source
AI summary
A method and system for text-based pose editing to generate a new pose from an initial pose and user-generated text includes an user input device for inputting the initial pose and the user-generated text; a variational auto-encoder configured to receive the initial pose; a text conditioning pipeline configured to receive the user-generated text; a fusing module configured to produce parameters for a prior Gaussian distribution Np; a pose decoder configured to sample the Gaussian distribution Np and generate, therefrom, the new pose; and an output device to communicate the generated new pose to a user. The variational auto-encoder and the text conditioning pipeline are trained using a PoseFix dataset, wherein the PoseFix dataset includes triplets having a source pose, a target pose, and text modifier.