💬 NLP

SIGNER: Temporally Grounded Sign Language Generation via Time-Resolved Conditioning

The paper introduces SIGNER, a sign language generation framework that addresses the limitations of existing methods by employing time-resolved conditioning and a local temporal fusion module to enforce temporal grounding, thereby achieving state-of-the-art performance in both lexical ordering and semantic accuracy on benchmark datasets.

Original authors: Taeryung Lee, Hyeongjin Nam, Gyeongsik Moon, Kyoung Mu Lee

Published 2026-06-29

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CC BY 4.0

Original authors: Taeryung Lee, Hyeongjin Nam, Gyeongsik Moon, Kyoung Mu Lee

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you are trying to teach a robot to tell a story using sign language. The robot needs to take a sentence written in English (like "I want to eat noodles today") and turn it into a sequence of hand movements and facial expressions.

The paper introduces a new system called SIGNER to do this. Here is the problem they are solving and how they fixed it, explained simply.

The Problem: The "Confused Chef"

Previous attempts to make robots sign were like a chef who has a recipe but throws all the ingredients into a giant pot at once.

The Recipe: The sentence "I want to eat noodles today" has a specific order. You can't say "noodles" before "I."
The Mistake: Old systems looked at the whole sentence at once and tried to guess the movements. Because they didn't pay attention to when each word should happen, the robot would often sign the words in the wrong order (like saying "noodles I want") or mix up the meaning of the gestures.
The Result: The robot's signing looked jumbled, and people couldn't understand it.

The Solution: The "Conductor with a Score"

The authors created SIGNER, which acts like a strict conductor with a musical score. Instead of looking at the whole song at once, the conductor looks at exactly which note needs to be played at this specific second.

They did this using two main tricks:

1. The "Time-Stamped Script" (Temporal-Gloss Condition)

First, the system takes the text and breaks it down into "glosses" (the sign language words).

Old Way: It just gave the robot a list of words: [I, want, noodles, today].
SIGNER's Way: It creates a timeline. It estimates how long each word should last and stretches the list out over time.
- Example: "I" gets 2 seconds, "want" gets 1 second, "noodles" gets 3 seconds.
- Now the robot has a script that says: "For the first 2 seconds, only think about 'I'. For the next second, only think about 'want'."

2. The "Local Spotlight" (Local Temporal Fusion)

This is the most important part. Imagine a stage with a spotlight.

Old Way (Global Fusion): The spotlight was a giant floodlight that lit up the entire stage at once. The robot could see all the words at the same time, which confused it about which word to sign right now.
SIGNER's Way (Local Temporal Fusion): The spotlight is a small, focused beam. It only shines on the specific word that is supposed to happen at this exact moment.
- When the robot is signing "I," the spotlight is on "I." The words "noodles" and "today" are in the dark.
- This prevents the robot from accidentally mixing up the order or blending the meanings of different words.

Why This Matters

By keeping the "spotlight" focused on just one word at a time, SIGNER ensures two things:

Correct Order: The robot signs the words in the exact order they appear in the sentence.
Clear Meaning: The robot doesn't mix up the gestures. "Noodles" looks like "noodles," not a weird mix of "noodles" and "today."

The Results

The authors tested SIGNER on two large datasets of sign language (one in Chinese, one in German).

Better than the competition: SIGNER beat all previous methods, including ones that tried to use similar ideas but didn't use the "local spotlight" trick.
Smoother Movements: The robot's movements were not just more accurate, but also smoother, without jerky transitions between words.
Robustness: Even if the system guessed the timing of a word slightly wrong (e.g., thinking "noodles" should take 3 seconds instead of 4), the system still worked well.

In a Nutshell

Previous robots were like people trying to speak a foreign language by shouting all the words at once. SIGNER is like a skilled actor who speaks one word at a time, perfectly timed to the script, ensuring the audience understands every single gesture in the right order.

Technical Summary: SIGNER - Temporally Grounded Sign Language Generation via Time-Resolved Conditioning

Problem Statement

Sign Language Generation (SLG), or text-to-sign generation, aims to bridge the communication gap between signers and non-signers by synthesizing sign gesture sequences from spoken-language text. Unlike general generative tasks (e.g., text-to-image), SLG is constrained by strict linguistic requirements:

Lexical Ordering: Sign language conveys meaning through a sequence of gestures aligned with word-like units called glosses. These segments must appear in the correct lexical order to preserve intended meaning.
Semantic Accuracy: Each gesture must faithfully reflect the specific gloss it represents.

Existing methods frequently fail to satisfy these constraints, producing signs with incorrect lexical orders and semantically ambiguous or mismatched segments. The authors identify the root cause as globally fused conditioning strategies. In prior approaches (e.g., using cross-attention over the entire sequence), conditioning signals (text or gloss features) are shared across all timesteps. This weakens temporal grounding—the precise temporal correspondence between a specific gloss and its realized sign segment—leading to cross-time confusion of lexical cues and mixed semantics.

Methodology: SIGNER

The authors propose SIGNER, a framework designed to enforce temporal grounding through time-resolved conditioning. The architecture consists of three main components:

1. Motion Latent Space Learning (PVQ-VAE)

To enhance the expressiveness of hands and faces, the authors utilize a Part-Aware Vector Quantized Variational Autoencoder (PVQ-VAE).

Representation: The input motion is decomposed into four parts: body, left hand, right hand, and face.
Encoding: Each part is processed by a dedicated 1D convolutional encoder and decoder with its own codebook.
Quantization: Continuous motion is mapped to discrete tokens via vector quantization, which are then used as the target for the diffusion process.

2. Temporal-Gloss Condition Construction

To provide time-resolved signals, the system constructs a condition sequence aligned with the temporal dimension:

Text-to-Gloss: Input text is converted into a gloss sequence using an off-the-shelf text-to-gloss model.
Duration Estimation: A learned MLP predicts the duration (in frames) for each gloss.
Sequence Construction: Gloss embeddings are repeated according to their estimated durations to form a time-resolved sequence $S$ . This ensures that specific gloss semantics are explicitly assigned to specific temporal windows.

3. Time-Resolved Conditioning with Local Temporal Fusion (LTF)

The core innovation is the Local Temporal Fusion (LTF) module, which replaces global fusion mechanisms (like standard cross-attention) during the diffusion denoising process.

Architecture: The denoiser processes body parts independently, except for an Inter-Part Attention (IPA) module that facilitates coordinated movements between body, hands, and face via cross-attention.
LTF Mechanism: Instead of attending to all gloss tokens globally, LTF integrates the temporal-gloss condition within a constrained local temporal window.
- It uses Adaptive Layer Normalization (AdaLN) to modulate features based on the specific gloss condition at the current token index.
- A 1D convolution aggregates local temporal context.
Effect: This design anchors gloss semantics to their corresponding token indices, preventing the mixing of linguistic cues across time and enforcing correct lexical ordering.

The model is trained using a standard discrete diffusion objective, minimizing the negative log-likelihood of ground-truth tokens conditioned on the temporal-gloss sequence.

Key Contributions

SIGNER Framework: A sign language generation framework that implements time-resolved conditioning to enforce lexical order and semantic accuracy.
Local Temporal Fusion (LTF): A novel fusion module that integrates temporal-gloss conditions locally during denoising. This preserves temporal grounding by restricting conditioning to local neighborhoods, thereby preventing the semantic overmixing common in global fusion approaches.
Enhanced Semantic Accuracy: By enforcing temporal locality, the method mitigates the ambiguity of sign segments, ensuring that each gesture faithfully reflects its intended gloss.

Experimental Results

The authors evaluated SIGNER on two large-scale datasets: Phoenix-2014T (German) and CSL-Daily (Chinese).

State-of-the-Art Performance: SIGNER outperforms prior methods (including MoMask, NAT-EA, and SOKE) on both datasets across all metrics.
- Motion Accuracy: Achieved lower Dynamic Time Warping Joint Position Error (DTW-JPE).
- Linguistic Fidelity: Significantly improved Word Error Rate (WER) for sign-to-gloss back-translation, and higher BLEU-4 and ROUGE scores for sign-to-text back-translation.
Ablation Studies:
- LTF vs. Global Fusion: Replacing LTF with global cross-attention or fully connected layers resulted in substantial performance drops, confirming the necessity of local temporal aggregation.
- Inter-Part Attention (IPA): Integrating attention flows between body, hands, and face (B→H, H→B, B→F) yielded the best results, highlighting the importance of coordinated articulation.
- Robustness: The system remained robust to variations in the text-to-gloss model choice and moderate perturbations in estimated gloss durations.
Motion Quality: SIGNER demonstrated superior motion smoothness, achieving the lowest peak velocity and peak jerk (indicating smoother transitions) compared to baselines.
Qualitative Analysis: Visual comparisons showed that while prior methods often produced signs with incorrect lexical orders or irrelevant segments, SIGNER generated accurate signs in the correct order.

Significance and Claims

The paper claims that the primary limitation of existing SLG methods is the global fusion of conditioning signals, which weakens temporal grounding. By introducing time-resolved conditioning via LTF, SIGNER directly addresses this limitation.

The authors assert that their work demonstrates that preserving temporal grounding during generation is critical for achieving correct lexical ordering and semantic accuracy. They show that simply incorporating gloss sequences (as done in some prior works) is insufficient if the conditioning mechanism remains globally fused. SIGNER's success suggests that explicit temporal alignment between gloss semantics and sign segments is a fundamental requirement for high-quality sign language generation.

The paper concludes modestly, acknowledging that while SIGNER performs well, it currently relies on off-the-shelf text-to-gloss models. Future work could explore jointly optimizing text-to-gloss and sign generation to reduce this dependency, particularly for new sign languages.