Sign Feature Aggregation Module

• The paper introduces a module that fuses pose, hand, and face signals via multi-scale 3D dilated convolutions to preserve linguistic sign content while decoupling signer identity.
• The approach clearly defines manual and non-manual channels, aggregating diverse temporal cues into a unified spatio-temporal representation for improved sign language production.
• Empirical ablations demonstrate that integrating the motion module with semantic features, using careful lambda weighting, enhances metrics like PSNR, SSIM, and LPIPS.

The Sign Feature Aggregation Module denotes the component $\Psi_{\text{motion}}$ introduced for sign language production in "Diverse Signer Avatars with Manual and Non-Manual Feature Modelling for Sign Language Production" (Lakhal et al., 21 Aug 2025). In that work, it is the central mechanism for injecting sign-specific linguistic information into a latent-diffusion avatar synthesis pipeline while separating sign content from signer appearance. The module aggregates three signer-centric modality streams—pose, hand, and face/head—into a fused spatio-temporal representation that is intended to preserve the content of the sign while being independent of the signer’s identity (Lakhal et al., 21 Aug 2025). More broadly, the term can also refer to a class of sign-oriented aggregation designs that explicitly combine manual and non-manual cues, multi-scale temporal structure, and cross-region or cross-stream interactions, although the exact name is not used uniformly across the literature (Hu et al., 2024, Gan et al., 16 Apr 2025).

1. Definition and conceptual role

In (Lakhal et al., 21 Aug 2025), the module $\Psi_{\text{motion}}$ sits between low-level modality extraction and the diffusion denoising backbone. The pipeline first preprocesses a sign video to extract signer-centric modalities, then encodes pose, hand, and face/head with separate encoders, and finally fuses these streams with $\Psi_{\text{motion}}$ before passing the result as part of the conditioning signal $c$ into a ControlNet/Stable-Diffusion-based latent denoiser. In parallel, a reference image $\mathbf{I}^r$ supplies target signer appearance, while the aggregation module provides the motion and language content to be rendered on that appearance (Lakhal et al., 21 Aug 2025).

The motivation arises from a specific critique of earlier sign language production systems. Prior GAN- and VAE-based signer synthesis methods are described as person-specific, affected by mode collapse, or unable to generalize across unseen signers and ethnicities. More recent diffusion-based human animation systems improve realism and diversity, but when applied directly to sign language they tend to miss linguistically critical channels: manual features such as hand shape, hand motion, and arm configuration, and non-manual features such as facial expression, mouthing, head movement, and emotional or grammatical cues (Lakhal et al., 21 Aug 2025). The aggregation module is introduced because standard pose-guided synthesis pipelines do not explicitly model the different spatial and temporal scales at which these cues occur.

This design establishes a division of labor within the full synthesis system. The reference branch contributes appearance, ethnicity, and texture, while $\Psi_{\text{motion}}$ contributes identity-independent sign structure. This suggests that the module is best understood not as a generic feature fusion block, but as a sign-content encoder specialized for avatar generation under appearance transfer (Lakhal et al., 21 Aug 2025).

2. Modality decomposition: manual and non-manual channels

The module is built on a concrete tripartite modality structure: pose, hand, and face/head (Lakhal et al., 21 Aug 2025). This decomposition is operational rather than probabilistic. The paper does not introduce special learned tokens or a formal disentangled latent-variable model; instead, it separates perceptual channels upstream and recombines them through structured fusion.

Manual features are represented mainly through hand and torso/pose cues. Non-manual features are represented mainly through facial expressions, and the supplementary material specifies that face preprocessing keeps only eyes and mouth regions using facial bounding boxes in order to retain linguistically relevant non-manual information while suppressing identity (Lakhal et al., 21 Aug 2025). The hand stream is likewise identity-reduced: PCA is applied to hand RGB values and projected to a single principal component, producing a compact representation that suppresses skin-color dependence.

The three modality-specific inputs are

$$\mathbf{x}_{\text{pose}}, \quad \mathbf{x}_{\text{hand}}, \quad \mathbf{x}_{\text{head}}.$$

Each is encoded separately: $$f_{\text{pose}} = \mathcal{E}_{\text{pose}}(\mathbf{x}_{\text{pose}}), \quad f_{\text{hand}} = \mathcal{E}_{\text{hand}}(\mathbf{x}_{\text{hand}}), \quad f_{\text{head}} = \mathcal{E}_{\text{head}}(\mathbf{x}_{\text{head}}) \in \mathbb{R}^{c \times t \times h \times w}.$$ The intended meaning given in the data is that $f_{\text{pose}}, f_{\text{hand}}, f_{\text{head}}$ are spatio-temporal tensors with channels $c$, time $\Psi_{\text{motion}}$0, and spatial dimensions $\Psi_{\text{motion}}$1, and that the encoders are implemented with an architecture similar to ControlNet (Lakhal et al., 21 Aug 2025).

This modality decomposition is central to the module’s signer-invariant objective. The paper repeatedly states that $\Psi_{\text{motion}}$2 aims to preserve the content of the sign while being independent of the signer’s identity, and the face and hand preprocessing choices directly support that goal (Lakhal et al., 21 Aug 2025).

3. Core architecture and aggregation equations

The core of $\Psi_{\text{motion}}$3 is a multi-scale 3D dilated convolutional aggregation over the three encoded modality streams (Lakhal et al., 21 Aug 2025). The module applies temporal-spatial kernels at dilation rates

$\Psi_{\text{motion}}$4

The rationale is explicit: small dilation captures local, fine-grained detail, especially face and other non-manual markers, while larger dilation captures coarse, wider-range motion patterns such as arm trajectories and hand movement.

The paper defines 3D dilated convolution over a feature tensor $\Psi_{\text{motion}}$5 as

$\Psi_{\text{motion}}$6

where $\Psi_{\text{motion}}$7 is the output spatio-temporal position, $\Psi_{\text{motion}}$8 indexes offsets in the kernel, and $\Psi_{\text{motion}}$9 is the dilation factor (Lakhal et al., 21 Aug 2025).

For each dilation rate, the convolved outputs over the three modalities are summed: $\Psi_{\text{motion}}$0 These $\Psi_{\text{motion}}$1 tensors are the module’s multi-scale motion summaries. In the later concatenation formula they appear as $\Psi_{\text{motion}}$2, corresponding to the three scales (Lakhal et al., 21 Aug 2025).

The module also models cross-feature interactions between manual and non-manual components. The encoded modality features are concatenated channel-wise and compressed with a $\Psi_{\text{motion}}$3 3D convolution followed by ReLU: $\Psi_{\text{motion}}$4 This cross-feature tensor is intended to capture coordinated relations among hand movement, torso orientation, and facial expression (Lakhal et al., 21 Aug 2025).

The final aggregation stage concatenates the original modality tensors, the cross-feature tensor, and the three multi-scale outputs: $\Psi_{\text{motion}}$5 and then fuses them through a final $\Psi_{\text{motion}}$6 convolution with a residual skip from the average of the original streams: $\Psi_{\text{motion}}$7 The residual term is reported to preserve original modality information and improve training stability, while the learned fusion builds richer sign-aware combinations (Lakhal et al., 21 Aug 2025).

4. Conditioning in latent diffusion and interaction with appearance control

The aggregated sign representation is not used alone. Within the full conditioning path, $\Psi_{\text{motion}}$8 is combined with the original modality features and with frozen Sapiens foundation-model features extracted from hand and face inputs (Lakhal et al., 21 Aug 2025). The conditioning fed to the denoising U-Net is

$\Psi_{\text{motion}}$9

Here $c$0 controls the strength of the aggregation module (Lakhal et al., 21 Aug 2025).

The diffusion objective is the standard conditional noise-prediction loss: $c$1 In this formulation, $c$2 is the noisy latent at timestep $c$3, $c$4 is the assembled condition, and $c$5 is the denoising U-Net (Lakhal et al., 21 Aug 2025).

The module’s relation to the reference-image stage is architecturally important. The sign content comes from the motion streams and $c$6; signer appearance comes from a separate reference image $c$7, which may be generated by a text-to-image model such as ControlNet. The architecture is described as following a design similar to AnimateAnyone/GPD-VVTO, with a reference-image branch processed by a separate network mirroring the denoiser (Lakhal et al., 21 Aug 2025). This separation is the mechanism by which the system attempts to preserve linguistic content while allowing diversity across signer appearance and ethnicity.

A common misconception is to treat the module as a complete disentanglement model. The paper does not define a formal disentangled latent variable model for manual and non-manual channels; the disentanglement is architectural and preprocessing-based rather than probabilistic (Lakhal et al., 21 Aug 2025).

5. Empirical behavior, ablations, and implementation details

The principal direct evidence for the module comes from ablation studies on YouTube-SL-25 (Lakhal et al., 21 Aug 2025). Using a CLIP appearance encoder and ViT feature extractor as baseline, the reported results are:

Setting	PSNR	SSIM	LPIPS
baseline	21.06	0.8458	0.1305
baseline + $c$8	21.36	0.8397	0.1329
baseline + $c$9	22.71	0.8635	0.1139

These numbers show that $\mathbf{I}^r$0 alone slightly improves PSNR but slightly worsens SSIM and LPIPS, while the combination with $\mathbf{I}^r$1 improves all three metrics (Lakhal et al., 21 Aug 2025). The interpretation given in the data is that aggregation without semantic regularization may overemphasize structural consistency without yet delivering the best perceptual fidelity, whereas the combined conditioning design performs substantially better.

The weighting factor $\mathbf{I}^r$2 is also ablated:

Setting	PSNR	SSIM	LPIPS
without $\mathbf{I}^r$3	21.06	0.8458	0.1305
$\mathbf{I}^r$4	21.33	0.8386	0.1336
$\mathbf{I}^r$5	21.34	0.8388	0.1333
$\mathbf{I}^r$6	21.36	0.8397	0.1329

The default retained value is $\mathbf{I}^r$7 (Lakhal et al., 21 Aug 2025). This suggests that, when used without the full semantic support of Sapiens features, the aggregation signal is useful but should be injected at relatively low weight.

Reproduction-relevant training details are scattered across the main paper and supplementary material. The system uses Stable Diffusion 2.1 extended with pose, hand, and face conditioning, plus aggregation and foundation-feature branches. Training proceeds in two phases: first, 30,000 iterations with batch size 64 using AdamW with learning rate $\mathbf{I}^r$8 and weight decay $\mathbf{I}^r$9 on four NVIDIA A100 GPUs; second, a temporal aggregation module following AnimateDiff is trained for 10,000 iterations using 24-frame clips and batch size 2 to improve temporal consistency (Lakhal et al., 21 Aug 2025). The diffusion process uses 1,000 timesteps, with early timesteps handling global pose and motion structure and later timesteps refining facial and hand detail.

The dataset preprocessing is equally specific. YouTube-SL-25 is preprocessed to isolate the primary signer, remove background clutter, and subsample up to 120 frames per video, yielding 13,300 training videos and 4,068 test videos. The signer is detected with YOLOv11s, background is matted, hand appearance is PCA-reduced, and facial input is cropped to preserve eyes and mouth (Lakhal et al., 21 Aug 2025).

6. Relation to adjacent aggregation designs in sign-language research

The exact term Sign Feature Aggregation Module is not standardized across sign-language research. In sign language production, (Lakhal et al., 21 Aug 2025) uses it explicitly for a multi-stream, multi-scale spatio-temporal fusion block. In other sign-language settings, closely related designs are described with different names.

In skeleton-aware sign language recognition, "Dynamic Spatial-Temporal Aggregation for Skeleton-Aware Sign Language Recognition" (Hu et al., 2024) does not use the same term, but its effective aggregation machinery comprises a Graph Correlation Module, Super Node Transform Module, Dynamic Graph Aggregation Module, and Parallel Temporal Convolution Modules. That system aggregates information across joints, latent super nodes, static learned joint relations, and multiple temporal scales. Its central claim is that strong sign recognition requires aggregation that is dynamic, long-range, importance-weighted, and multi-scale in time (Hu et al., 2024).

In RGB-based sign-language tasks, "Graph Network for Sign Language Tasks" (Gan et al., 16 Apr 2025) likewise avoids the same label, but introduces Local Sign Graph (LSG), Temporal Sign Graph (TSG), and Hierarchical Sign Graph (HSG) modules. These respectively target intra-frame cross-region aggregation, inter-frame cross-region aggregation, and same-region cross-scale aggregation (Gan et al., 16 Apr 2025). This provides a useful contrast: (Lakhal et al., 21 Aug 2025) aggregates pose, hand, and face/head streams for controllable generation, whereas (Gan et al., 16 Apr 2025) aggregates patch-node relations for recognition and translation.

A plausible implication is that the phrase sign feature aggregation has become a descriptive umbrella rather than a single fixed architectural recipe. Across these works, three recurrent themes appear: explicit handling of sign-relevant structure, multi-scale temporal or spatial reasoning, and fusion of complementary channels or regions. What differs is the representational substrate—pose-hand-face tensors in (Lakhal et al., 21 Aug 2025), skeleton graphs in (Hu et al., 2024), and RGB patch graphs in (Gan et al., 16 Apr 2025).

7. Interpretation, scope, and limitations

The sign feature aggregation module in (Lakhal et al., 21 Aug 2025) is best characterized as a multi-stream, multi-scale spatio-temporal fusion block that converts identity-reduced pose, hand, and face cues into a signer-invariant representation of sign content. Its technical novelty lies in combining separate manual and non-manual encoders, 3D dilated convolutions at multiple receptive fields, explicit cross-modal fusion, and residual aggregation, then using the result as a controllable condition for latent diffusion avatar synthesis (Lakhal et al., 21 Aug 2025).

At the same time, the paper identifies several boundaries of what the module does not provide. It does not define special learned token types, does not specify exact landmark parameterizations or keypoint vector formats, and does not present a formal disentangled latent-variable model of manual and non-manual channels. Lower-level design specifics such as exact encoder layer layouts, tensor shapes per stage, and the exact Sapiens extraction pipeline are not fully specified in the text (Lakhal et al., 21 Aug 2025). For that reason, the module is conceptually clear and equation-level explicit, but not fully exhaustively documented at implementation granularity.

Another possible misconception is that the module alone explains the system’s final perceptual performance. The ablations indicate otherwise: $\Psi_{\text{motion}}$0 is beneficial, but it works best as part of a larger conditioning design that also includes Sapiens semantic features and a separate reference-image pathway (Lakhal et al., 21 Aug 2025). In that sense, the module is central but not sufficient in isolation.

Taken in the narrower sense established by (Lakhal et al., 21 Aug 2025), the Sign Feature Aggregation Module is a conditioning mechanism for signer-diverse sign avatar generation. Taken in the broader sense suggested by adjacent work, it names a family of sign-oriented aggregation strategies whose defining property is explicit fusion of linguistically relevant structure rather than generic visual feature pooling (Hu et al., 2024, Gan et al., 16 Apr 2025).