Text2Sign Diffusion: Gloss-Free Sign Language Production

• Text2Sign Diffusion is a sign language production framework that directly maps spoken sentences to sign pose frames without using gloss annotations.
• It employs a transformer-based SignVAE, a cross-modal signing aligner, and latent diffusion to overcome weak linguistic-to-visual translation and ensure semantic fidelity.
• The framework demonstrates state-of-the-art performance on PHOENIX14T and How2Sign by mitigating error accumulation typical in gloss-mediated systems.

Searching arXiv for the specified paper and closely related sign-language diffusion work. I’m going to look up the named papers on arXiv to ground the article in current preprints. Searching "Text2Sign Diffusion gloss-free sign language production" Text2Sign Diffusion (Text2SignDiff) is a gloss-free sign language production (SLP) framework that maps a spoken-language sentence directly to a sequence of sign-language pose frames through a latent diffusion process, without using gloss annotations as an intermediate representation during training or inference (Feng et al., 13 Sep 2025). In the formulation reported for the method, a spoken sentence $$\mathbf{X} = \{\mathbf{x}_1, \mathbf{x}_2, \dots, \mathbf{x}_W\}$$ is mapped to a sign pose sequence $$\mathbf{P} = \{\mathbf{p}_1, \mathbf{p}_2, \dots, \mathbf{p}_U\}$$, where each pose frame $\mathbf{p}_i$ contains $K$ keypoints and each keypoint $\mathbf{k}_j^i \in \mathbb{R}^d$ (Feng et al., 13 Sep 2025). The system combines three components—SignVAE, a Cross-Modal Signing Aligner, and a gloss-free latent diffusion model—to address two central difficulties identified for direct text-to-pose generation: weak end-to-end linguistic-to-visual translation and poor sentence-level semantic alignment (Feng et al., 13 Sep 2025).

1. Problem setting and conceptual basis

Text2SignDiff is situated within sign language production rather than sign language translation. Its target output is a temporal sequence of skeletal pose frames rather than raw pixels, gloss strings, or spoken-language text (Feng et al., 13 Sep 2025). The model is explicitly introduced against the conventional pipeline

$$\text{text} \rightarrow \text{gloss} \rightarrow \text{pose},$$

which is described as limiting because gloss annotation is expensive and scarce, glosses are language-specific, glosses create an information bottleneck, and multi-stage systems suffer from error accumulation (Feng et al., 13 Sep 2025).

Within this framing, “gloss-free” means that the system does not use gloss annotations as an intermediate representation during either training or inference. Supervision consists only of paired spoken-language sentences and sign pose sequences (Feng et al., 13 Sep 2025). The paper’s stated objective is not merely lexical substitution but the generation of coherent, temporally smooth, semantically faithful signing (Feng et al., 13 Sep 2025).

The method operates on a skeletal sign representation with 79 keypoints per frame: 21 keypoints for each hand, 11 for the body, and 26 for the face (Feng et al., 13 Sep 2025). The maximum sequence length is $U = 256$ frames. Sequences are padded with a special End-of-Sign frame, and inference truncates output at the first detected end token (Feng et al., 13 Sep 2025). This output representation places Text2SignDiff in the pose-generation branch of sign synthesis rather than in downstream signer rendering systems.

A central claim of the work is that direct gloss-free generation can outperform gloss-mediated systems if semantic alignment is handled effectively (Feng et al., 13 Sep 2025). This position is methodologically significant because it shifts the burden of alignment from explicit gloss supervision to learned shared latent structure between text and sign.

2. System architecture

The architecture reported for Text2SignDiff has three major components: SignVAE, a Cross-Modal Signing Aligner, and a gloss-free latent diffusion model (Feng et al., 13 Sep 2025). The overall flow is

$$\text{text} \rightarrow \text{sentence embedding} \rightarrow \text{conditional latent diffusion} \rightarrow \text{latent sign code} \rightarrow \text{SignVAE decoder} \rightarrow \text{pose sequence}.$$

SignVAE, denoted $\mathcal{V}=\{\mathcal{E},\mathcal{D}\}$, is a transformer-based variational autoencoder that compresses sign pose sequences into a structured latent space (Feng et al., 13 Sep 2025). The encoder maps poses to a latent sign code,

$\mathbf{z}_0 = \mathcal{E}(\mathbf{P}),$

and the decoder reconstructs poses from that code,

$$\mathbf{P} = \{\mathbf{p}_1, \mathbf{p}_2, \dots, \mathbf{p}_U\}$$0

The latent space dimension reported for SignVAE is 20, and SignVAE is pretrained for 1000 epochs and then frozen for subsequent stages (Feng et al., 13 Sep 2025).

Text conditioning is sentence-level rather than gloss-level or word-level. The text encoder is Sentence-BERT, specifically distiluse-base-multilingual-cased-v2, which maps each sentence to a 512-dimensional embedding (Feng et al., 13 Sep 2025). The paper denotes this encoder as $$\mathbf{P} = \{\mathbf{p}_1, \mathbf{p}_2, \dots, \mathbf{p}_U\}$$1 and the sentence condition as

$$\mathbf{P} = \{\mathbf{p}_1, \mathbf{p}_2, \dots, \mathbf{p}_U\}$$2

The Cross-Modal Signing Aligner is designed to bridge sign latent representations and spoken-language sentence embeddings through a shared latent space (Feng et al., 13 Sep 2025). It consists of a Pose Aligner $$\mathbf{P} = \{\mathbf{p}_1, \mathbf{p}_2, \dots, \mathbf{p}_U\}$$3 and a Text Aligner $$\mathbf{P} = \{\mathbf{p}_1, \mathbf{p}_2, \dots, \mathbf{p}_U\}$$4, producing

$$\mathbf{P} = \{\mathbf{p}_1, \mathbf{p}_2, \dots, \mathbf{p}_U\}$$5

The shared latent dimension is reported as $$\mathbf{P} = \{\mathbf{p}_1, \mathbf{p}_2, \dots, \mathbf{p}_U\}$$6 (Feng et al., 13 Sep 2025). This aligner is pretrained with a contrastive objective and then frozen during diffusion training.

The latent diffusion module operates not on raw poses but in the 20-dimensional SignVAE latent space (Feng et al., 13 Sep 2025). Its denoising backbone is described as a U-Net with skip connection, and text conditioning is injected by cross-attention following latent diffusion design (Feng et al., 13 Sep 2025). The paper does not report a deeper module-by-module specification of the temporal layers inside the SignVAE or U-Net, nor the number of layers, heads, or hidden widths for these blocks (Feng et al., 13 Sep 2025).

3. Mathematical formulation

The SignVAE objective is given as

$$\mathbf{P} = \{\mathbf{p}_1, \mathbf{p}_2, \dots, \mathbf{p}_U\}$$7

This combines pose reconstruction with KL regularization to a unit Gaussian prior (Feng et al., 13 Sep 2025). The latent code produced by SignVAE becomes the diffused variable for the later DDPM stage.

The Cross-Modal Signing Aligner is trained with an InfoNCE-style contrastive loss based on cosine similarity:

$$\mathbf{P} = \{\mathbf{p}_1, \mathbf{p}_2, \dots, \mathbf{p}_U\}$$8

with

$$\mathbf{P} = \{\mathbf{p}_1, \mathbf{p}_2, \dots, \mathbf{p}_U\}$$9

This objective pulls matched pose-text pairs together and pushes mismatched pairs apart within the batch (Feng et al., 13 Sep 2025).

The forward diffusion process in latent space is specified as

$\mathbf{p}_i$0

with factorization

$\mathbf{p}_i$1

Here $\mathbf{p}_i$2 is the clean sign latent code, $\mathbf{p}_i$3 is the noisy latent at timestep $\mathbf{p}_i$4, and $\mathbf{p}_i$5 is the noise-schedule hyperparameter (Feng et al., 13 Sep 2025).

The reverse conditional denoising process is defined by

$\mathbf{p}_i$6

with

$\mathbf{p}_i$7

and

$\mathbf{p}_i$8

The denoiser is trained in the standard noise-prediction parameterization,

$\mathbf{p}_i$9

This places Text2SignDiff squarely within conditional latent DDPM-style generation rather than direct autoregressive pose prediction (Feng et al., 13 Sep 2025).

A distinctive part of the formulation is the semantic supervision supplied by the pretrained aligner during diffusion. The paper defines

$K$0

and a cosine-distance semantic loss

$K$1

The combined diffusion-stage objective is

$K$2

The time-dependent factor is explicitly interpreted in the paper as stronger semantic guidance in earlier denoising stages (Feng et al., 13 Sep 2025). This suggests that the model treats coarse semantic steering and fine denoising as partially distinct phases of generation.

4. Training procedure and inference

Training is staged rather than end-to-end from scratch. The first stage pretrains SignVAE for 1000 epochs on sign pose sequences using $K$3; the resulting 20-dimensional latent space is then frozen (Feng et al., 13 Sep 2025). The second stage pretrains the Cross-Modal Signing Aligner for 1000 epochs using latent sign codes from the frozen SignVAE and sentence embeddings from Sentence-BERT, optimizing $K$4 in a shared 512-dimensional space (Feng et al., 13 Sep 2025). The third stage trains the latent diffusion model for 1000 epochs with batch size 256, Adam, and learning rate $K$5, while both SignVAE and the aligner remain frozen (Feng et al., 13 Sep 2025).

The diffusion setup uses 1000 timesteps and a linear noise schedule with $K$6 linearly spaced from $K$7 to $K$8 (Feng et al., 13 Sep 2025). The conditioning strategy remains sentence-level throughout, with Sentence-BERT embeddings injected into the denoising U-Net via cross-attention (Feng et al., 13 Sep 2025).

At inference time, generation begins by encoding the input text:

$K$9

The latent sign code is initialized from Gaussian noise,

$\mathbf{k}_j^i \in \mathbb{R}^d$0

and reverse denoising iteratively transforms this noisy latent into $\mathbf{k}_j^i \in \mathbb{R}^d$1 (Feng et al., 13 Sep 2025). The final latent is decoded by the SignVAE decoder,

$\mathbf{k}_j^i \in \mathbb{R}^d$2

to produce the pose sequence, after which the End-of-Sign frame is used for truncation (Feng et al., 13 Sep 2025). Because inference begins from Gaussian noise and proceeds through a sampled reverse chain, generation is stochastic rather than deterministic (Feng et al., 13 Sep 2025).

The paper states that generation takes only seconds but is still not suitable for real-time use (Feng et al., 13 Sep 2025). It also notes that higher frame rates would be needed for smoother motion in applications, and that the current model is limited to sequences of at most 256 frames (Feng et al., 13 Sep 2025).

5. Empirical results and ablation evidence

Text2SignDiff is evaluated on PHOENIX14T and How2Sign (Feng et al., 13 Sep 2025). PHOENIX14T is described as derived from German weather broadcasts, with 8,257 sign pose clips and a vocabulary of 2,887 German words (Feng et al., 13 Sep 2025). How2Sign is described as an American Sign Language dataset with over 34,000 clips and more than 16,000 English words (Feng et al., 13 Sep 2025).

Evaluation is conducted on generated sign pose sequences rather than rendered videos. Semantic faithfulness is measured by back-translation: a pretrained sign language translation model converts generated pose sequences back into spoken text, after which BLEU-1, BLEU-2, BLEU-3, BLEU-4, and ROUGE are computed against the ground-truth sentence (Feng et al., 13 Sep 2025). The paper also reports mean joint error with Dynamic Time Warping (DTW) for spatial and temporal alignment (Feng et al., 13 Sep 2025).

On PHOENIX14T, Text2SignDiff reports BLEU-4 11.23, BLEU-3 15.18, BLEU-2 20.91, BLEU-1 32.65, ROUGE 33.08, and DTW 0.0997, exceeding the listed results for Progressive Transformers, GEN, DET, NAT-EA, G2P-DDM, CasDual-Transformer, and Data-Driven Representation SLP (Feng et al., 13 Sep 2025). On How2Sign, it reports BLEU-4 5.68, BLEU-3 8.28, BLEU-2 12.11, BLEU-1 17.91, ROUGE 18.33, and DTW 0.1051, again outperforming the listed baselines Progressive Transformers, Progressive Transformers w/ MS, Ham2Pose, T2M-GPT, and MS2SL-T2S (Feng et al., 13 Sep 2025). The paper therefore claims state-of-the-art performance on both benchmarks (Feng et al., 13 Sep 2025).

The ablation study isolates the role of the aligner and the time-weighted semantic loss. Removing pose-text aligner pre-training drops PHOENIX14T BLEU-4 from 11.23 to 8.32 and How2Sign BLEU-4 from 5.68 to 3.29 (Feng et al., 13 Sep 2025). Removing the factor $\mathbf{k}_j^i \in \mathbb{R}^d$3 degrades performance on both datasets, with PHOENIX14T BLEU-4 falling to 9.91 and How2Sign BLEU-4 to 4.97 (Feng et al., 13 Sep 2025). Diffusion without the pose-text aligner performs substantially worse still, with PHOENIX14T BLEU-4 6.35 and How2Sign BLEU-4 3.17 (Feng et al., 13 Sep 2025). These results support the paper’s claim that the cross-modal aligner is central rather than auxiliary.

Qualitative examples are described as showing more expressive and semantically appropriate motion than ablated variants, including snow-related waving motion and a face-washing gesture around the face (Feng et al., 13 Sep 2025). A plausible implication is that the shared sign-sentence latent space contributes not only to sentence-level semantic fidelity but also to the selection of localized motion patterns tied to lexical content.

6. Relation to adjacent diffusion-based sign generation systems

Text2SignDiff belongs to a broader but heterogeneous set of diffusion-based sign-language systems. Among the most direct antecedents is the “Text-driven Diffusion Model” (TDM), which also performs direct text-to-sign generation without an intermediate gloss sequence and frames the task as continuous sign language production from text on PHOENIX14T (He et al., 20 Mar 2025). TDM differs structurally from Text2SignDiff in several reported respects: it diffuses directly in pose space rather than SignVAE latent space, uses an $\mathbf{k}_j^i \in \mathbb{R}^d$4-prediction denoiser that predicts the clean pose sequence instead of the noise, and trains with a joint loss $\mathbf{k}_j^i \in \mathbb{R}^d$5 plus a bone orientation loss $\mathbf{k}_j^i \in \mathbb{R}^d$6 rather than a semantic alignment loss supplied by a pretrained cross-modal aligner (He et al., 20 Mar 2025). TDM reports BLEU-1 = 20.17 and second place in the SLRTP Sign Production Challenge, together with the best DTW = 0.0492 among listed teams (He et al., 20 Mar 2025). Relative to that design, Text2SignDiff emphasizes sentence-level latent semantic alignment and latent-space diffusion.

“SignDiff: Diffusion Model for American Sign Language Production” addresses American Sign Language production as a two-stage text-to-sign generation problem, but its diffusion component is used in the pose-to-video rendering stage rather than in the initial text-to-pose stage (Fang et al., 2023). The reported pipeline is

$\mathbf{k}_j^i \in \mathbb{R}^d$7

with an ASL-SLP Transformer for text-to-pose and a dual-condition diffusion pre-training model for pose-to-video generation (Fang et al., 2023). As described, SignDiff is therefore a hybrid Text2Pose plus conditional diffusion rendering system rather than a pure text-conditioned diffusion model end-to-end (Fang et al., 2023). This distinction clarifies the specific contribution of Text2SignDiff: its diffusion model is the text-conditioned generator of sign pose sequences themselves.

“DiffSign: AI-Assisted Generation of Customizable Sign Language Videos With Enhanced Realism” is also centered on the downstream rendering stage rather than direct text-to-pose generation (Krishnamurthy et al., 2024). Its pipeline extracts signing keypoints from a human sign-language video, retargets them to SMPL-X, renders an avatar in Blender, and conditions Stable Diffusion with ControlNet and IP-Adapter to generate a realistic synthetic signer (Krishnamurthy et al., 2024). The paper explicitly positions this as attachable to any upstream “language2pose” model rather than as a text-to-sign system in its own right (Krishnamurthy et al., 2024). This suggests a modular decomposition of the broader text-to-sign problem into a linguistic generation stage and a signer-rendering stage, with Text2SignDiff occupying the former.

“DiffSLT: Enhancing Diversity in Sign Language Translation via Diffusion Model” is not a production system at all but a sign-language-to-text translation model (Moon et al., 2024). Its relevance is architectural rather than task-level: it shows how diffusion can be used in a compressed latent space with cross-attention conditioning, guidance fusion, and optional intermediate symbolic structure (Moon et al., 2024). A plausible implication is that Text2SignDiff and related SLP systems belong to a wider trend in sign-language research toward latent diffusion over structured continuous representations rather than raw-pixel synthesis or strictly autoregressive token generation.

Within this landscape, Text2SignDiff is specifically characterized by four reported properties: fully gloss-free supervision, sentence-level text conditioning with Sentence-BERT, latent diffusion in a pretrained 20-dimensional sign space, and semantic control through a frozen cross-modal signing aligner (Feng et al., 13 Sep 2025). Its reported results on PHOENIX14T and How2Sign support the claim that direct gloss-free text-to-pose generation can outperform both gloss-based and gloss-free baselines when sentence-level alignment is made an explicit part of the objective (Feng et al., 13 Sep 2025).