Cascade Dual-Decoder Sign Pose Generation for Natural Sign Language
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Solution Overview
Problem
Existing Sign Language Production (SLP) models struggle to accurately generate precise and natural sign language expressions due to asynchronous multi-articulatory characteristics and differences in tokenization and phonological properties between voice and sign languages, often regressing to average hand shapes and accumulating errors in continuous predictions.
Innovation Solution
A cascade dual-decoder system comprising a text encoder, hand pose decoder, and sign pose decoder, which aligns text and hand movements through attention layers and employs a space-time loss function to enhance accuracy and naturalness of sign language generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single decoder is used for sign language production, then the device complexity is low, but the manufacturing precision of hand expressions deteriorates due to regression toward average hand shape
Solution Approach 1:
The patent divides the single decoder into two separate decoders: a hand pose decoder that processes hand-related information and a sign pose decoder that processes non-hand information. This segmentation allows each decoder to specialize in specific aspects of sign language production, preventing the regression toward average hand shape while maintaining manageable system complexity.
Solution Approach 2:
The patent introduces a new dimension by adding the hand channel as a separate processing stream. The hand pose decoder operates in this additional dimension, allowing independent optimization of hand expression precision without being constrained by the single decoder's need to balance all aspects of sign language production.
2Device complexity
If spatial regression is used for sign language generation, then the device complexity remains simple, but the manufacturing precision of sign language motions deteriorates due to lack of temporal context
Solution Approach 1:
The patent implements continuous prediction across time steps by processing sequences of text inputs and generating corresponding sequences of hand and sign poses. The model maintains temporal continuity by considering previous predictions and current inputs together, ensuring that sign language motions are generated with proper temporal context rather than as isolated spatial snapshots.
3Device complexity
If a single decoder processes both hand and non-hand elements, then the device complexity is low, but the manufacturing precision of full channel sign pose deteriorates due to error accumulation
Solution Approach 1:
The patent segments the processing of hand and non-hand elements into separate decoder streams. The hand pose decoder handles hand-related keypoints while the sign pose decoder handles non-hand keypoints, reducing error accumulation by preventing cross-contamination of errors between these different types of movements.
Solution Approach 2:
The patent introduces an intermediary mechanism where the hand pose decoder's output serves as input to the sign pose decoder. This intermediary relationship allows the hand channel information to guide the non-hand channel prediction, improving overall full channel sign pose accuracy while maintaining a relatively simple two-decoder architecture.
Data Source
AI summary
Provided is a cascade dual-decoder based sign language producing device and method, and the device includes a text encoder configured to input a text sequence prepared in advance into at least one encoder block to output contextual features, a hand pose decoder configured to input the contextual features output from the text encoder and a hand pose sequence prepared in advance into at least one attention layer to output a hand-channel sign pose feature that aligns text and a hand motion, and a sign pose decoder configured to input the contextual features output from the text encoder and the hand pose decoder, the hand-channel sign pose feature, and a sign pose sequence prepared in advance into at least one attention layer to output a full-channel sign pose sequence in which the sign language is implemented as a hand element and a non-hand element.