PaperlayerView exact revision on arXiv

Source-linked AI summary

SignRefine: Adapting Foundational Video Models for Sign Language Generation

Anton Pelykh, Edward Fish, Ozge Mercanoglu Sincan, Richard Bowden

arXiv:2609.08496v1cs.CV

TL;DR

Sign language video generation requires fine hand and facial articulation, but existing approaches face limits in signing fidelity or generalization. SignRefine adapts a pretrained video DiT with regional adapters and introduces NVSign, a large-scale dataset of native signing. The paper reports better performance than baselines and comprehensible, diverse outputs in quantitative evaluation and a user study.

  • Problem

    Specialized sign-language video models trained on small, constrained datasets generalize poorly, while foundational video models struggle with the domain shift to continuous signing.

    Source: §1 · p. 2

  • Method

    SignRefine adapts a pretrained video DiT, injecting separate high-resolution regional conditions for the face and hands through spatially grounded local adapters.

    Source: §4.1 · p. 7, §7 · p. 15

  • Results

    SignRefine outperforms baselines and generates comprehensible, diverse sign-language videos, according to quantitative evaluation and a user study with sign language users.

    Source: §1 · p. 3, §7 · p. 15

  • Takeaways & Limitations

    NVSign’s authentic multi-party conversations support research on multi-signer dynamics, turn-taking, and non-manual discourse markers.

    Source: §1 · p. 3

    Generating an 81-frame clip takes minutes on high-end GPUs, and conventional-frame-rate training data can cause motion blur during fast signing.

    Source: §6 · p. 15

Abstract

from arXiv · show

Sign language video generation demands precise hand and facial articulation, yet modern video diffusion models, trained predominantly on spoken-language video, produce artifacts that render signing unintelligible. We propose SignRefine, a sign language video generation model that produces comprehensible signing from 2D keypoint conditioning alone, generalizing across appearances and visual conditions. Our approach builds on a pretrained video diffusion transformer and introduces local adapters with spatial grounding to selectively refine hand and face regions, steering the strong base model's prior toward accurate articulation. To enable this work and support broader sign language research, we present NVSign, a large-scale dataset of video content natively produced in sign language, offering diverse signer appearances, environments, and natural conversational settings. Trained on this data, our model shows up to 30% improvement in hand pose precision metrics over the strongest baseline and is preferred by sign language users for visual quality and comprehensibility in more than 80% of comparisons.

1 Introduction

Sign language video generation must capture fine hand and facial articulation, which general-purpose diffusion models and full-body conditioning do not reliably preserve. SignRefine adapts a pretrained video DiT with spatially grounded local adapters, alongside the introduction of NVSign, a large-scale dataset of native sign-language content.

1 Introduction

  • General-purpose video diffusion models struggle with signing because hands and faces carry linguistic meaning but occupy little frame area and are compressed by latent patchification.The paper also contrasts specialized models trained on small, constrained datasets with foundation models that generalize visually but cannot handle the domain shift to continuous signing.Source: §1 · pp. 1–2
  • SignRefine adapts a pretrained video DiT using separate, spatially grounded conditions for the face and each hand, injected through local cross-attention adapters.The approach uses CoordConv for region locations and learned sink tokens to route background queries away from the foreground.Source: §1 · pp. 2–3
  • NVSign is a large-scale dataset of native sign-language content with diverse signer appearances, visual environments, and natural conversations.Its multi-participant conversations can support research on multi-signer dynamics, turn-taking, and non-manual discourse markers.Source: §1 · p. 3
  • SignRefine outperforms baselines and generates comprehensible, diverse sign language videos, according to quantitative evaluation and a user study with sign language users.The stated contributions do not provide specific metric values or preference percentages.Source: §1 · p. 3

2 Related Work

Related work progresses from general video-diffusion methods and human-animation systems to specialized sign-language generation. The paper positions SignRefine as adapting a pretrained video model while conditioning on sparse skeleton keypoints.

Video Generation Models

  • Video diffusion models advanced from pixel-space to latent-space generation, while controllability methods and DiT backbones enabled longer, consistent videos with complex interactions.Control mechanisms developed for U-Nets can also be adapted to DiT architectures.Source: §2 · pp. 3–4

Human Image Animation

  • Human-animation systems using pretrained image and video models handle general movement and global pose alignment but struggle with the articulated hands and nuanced facial expressions needed for signing.The cited approaches include systems using frozen latent diffusion models, separate reference-image and keypoint pathways, and DiT backbones.Source: §2 · p. 4

Sign Language Video Generation

  • Sign-language video models progressed from pose-conditioned GANs to diffusion models, but small training datasets limit generalization and resolution; some methods also require dense structural inputs.SignRefine instead adapts a pretrained video diffusion model and uses sparse skeleton keypoints.Source: §2 · p. 4

3 NVSign Dataset

NVSign addresses gaps in existing sign-language datasets with native signing, natural multi-party conversations, and varied visual conditions. It provides substantial video volume and multimodal annotations, with partitions designed to support nuanced evaluation.

3 NVSign Dataset

  • NVSign covers multiple dynamic camera views, signers, environments, contexts, and topics.The table caption defines dynamic camera footage as involving moving or switching camera angles.Source: Table 1 · p. 5
  • Existing broadcast, web-scraped, and lab-recorded datasets lack, respectively, natural signing conditions, verified profiles and conversational interaction, or varied real-world visuals.NVSign is presented as a large-scale dataset of content natively produced in sign language.Source: §3 · p. 5
  • NVSign contains 44,759 signing segments from 317 videos, totaling 78.1 hours and an estimated 2,184 unique signers.Approximately 40% of the dataset consists of scenes with two or more participants.Source: §3 · p. 5
  • NVSign provides aligned English transcripts, 2D body keypoints, per-signer activity scores, depth maps, and SMPL, MANO, and FLAME parameters.The dataset also offers three partitions that trade off signer overlap against content diversity.Source: §3 · pp. 5–6

4 Methodology

SignRefine augments a frozen, skeleton-conditioned video DiT with spatially grounded local adapters for hands and face. Regional conditioning, masked injection, and weighted training target precise articulation while retaining global video coherence.

4 Methodology

  • Trainable local adapters extract higher-resolution hand and face conditions and inject them into selected blocks of a frozen skeleton-conditioned DiT.The frozen backbone preserves its generalized video priors while adapters steer generation toward faithful articulation.Source: §4 · p. 6

4.1 Regional Conditions with Spatial Grounding

  • Separate face and hand encoders process 256 × 256 regional keypoint videos into motion tokens, while CoordConv-style coordinates map crops to full-frame positions.Causal convolutions compress time from T frames to T/4 to align with the backbone latent resolution.Source: §4.1 · p. 7

4.2 Localized Feature Injection

  • Masked cross-attention injects regional motion features at staggered DiT blocks; foreground patches attend to articulator tokens while background queries go to a sink token.Left- and right-hand conditions are concatenated before injection to resolve interhand dependencies and occlusions.Source: §4.2 · pp. 7–8

4.3 Adapter Training

  • Training uses region-weighted noise-prediction MSE, setting hand and face weights to 10 versus 1 for background; torso areas outside articulator boxes remain in the background region.The weights raise the articulators’ gradient share while retaining roughly a third of the gradient on the background; hands take priority in overlapping regions.Source: §4.3 · pp. 8–9

5 Experiments

Experiments evaluate regional generation quality across datasets, assess participant preferences and comprehension, and test adapter components. Results favor SignRefine and support sparse regional conditioning.

5.1 Implementation Details

  • The evaluation uses a frozen Wan2.1-1.3B-Fun-Control backbone, with separate face and hand adapters trained on about 40,000 NVSign clips for 30,000 iterations.The study evaluates 100 randomly selected videos from each of NVSign, Phoenix14T, and BSL Corpus, using regional perceptual and keypoint metrics.Source: §5.1 · p. 9, §5.2 · p. 9

5.2 Quantitative Evaluation

  • Roughly 30% lower hand MPJPE on NVSign than Wan-FC, with face precision gains up to 20% on Phoenix14T and 15% on BSL Corpus.The reported comparison covers three anatomical regions across three datasets.Source: Table 2 · p. 11, §5.2 · p. 11
  • SignRefine maintains stable performance across datasets without dataset-specific adaptation, while SignViP hand MPJPE roughly doubles out of domain and SignGAN performs weakest overall.The reported out-of-domain collapse for SignViP is on NVSign and BSL Corpus relative to its in-domain results.Source: §5.2 · p. 11

5.3 User Study

  • Under the balanced exclusion schedule, the study’s method achieves the lowest participant-assigned mean rank overall and across both question types, with every baseline performing worse in Holm-corrected Wilcoxon tests (p < 0.001).Source: §5.3 · p. 12, Table 3 · p. 12
  • In a comprehension study of 60 responses from six fluent BSL signers, our model outperformed Wan-FC on all four metrics; Keyword Recall and BLEURT were significant (p<0.05).LLM adequacy and Failure Rate showed the same effect as a consistent trend; the authors noted that their panel was small.Source: Table 4 · p. 12, §5.3 · pp. 13–14

5.4 Ablation Study

  • Dense conditions marginally improve face metrics but not hand metrics; sparse keypoints are the default, avoiding inference-time MANO and face-normal prediction and rendering.The authors attribute hand quality primarily to 256×256 condition magnification and the pretrained backbone's hand prior.Source: §5.4 · p. 14
  • Removing regional attention masking worsens metrics, including face MPJPE from 1.770 to 1.949 (+10.1%); removing CoordConv causes smaller, consistent degradation (+4.3%).The authors attribute the masking effect to focusing cross-attention on relevant spatial regions.Source: Table 5 · p. 14, §5.4 · p. 14

6 Limitations

SignRefine has practical constraints in generation speed, fast-motion detail, and dependence on estimated keypoints.

6 Limitations

  • Generating an 81-frame clip takes minutes even on high-end GPUs, precluding real-time applications.The model reproduces motion blur in training videos, typically filmed at 25 fps, and can smear fine hand details during rapid transitions.Source: §6 · p. 15
  • Errors from off-the-shelf pose estimators can mislead generation when input keypoints are noisy.Errors in predicted keypoints also add noise to reference-based evaluation metrics, potentially obscuring performance differences.Source: §6 · p. 15
  • Motion blur in training data can smear fine hand details during rapid transitions.The limitation is linked to training videos typically filmed at conventional frame rates of 25 fps.Source: §6 · p. 15

7 Conclusion

SignRefine adapts a pretrained Diffusion Transformer for sign language generation, and NVSign contributes diverse native signing data. The model improves hand pose precision over the strongest baseline and is preferred by sign language users in over 80% of pairwise comparisons.

7 Conclusion

  • Sign language users prefer SignRefine in over 80% of pairwise comparisons, and it improves hand pose precision over the strongest baseline.The conclusion describes the hand pose precision improvement as substantial.Source: §7 · p. 15
  • SignRefine adapts a pretrained Diffusion Transformer with spatially grounded local adapters for sign language video generation.High-resolution hand and face conditions enter the frozen backbone through masked cross-attention to correct articulation failures without sacrificing quality and generalisability.Source: §7 · p. 15
  • NVSign is a large-scale, high-resolution native-sign dataset with diverse appearances, visual conditions, and multi-party conversations.The conclusion describes conversational scenes as absent from prior corpora.Source: §7 · p. 15

Related papers

Loading 2609.08496v1…