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arXiv · 2606.24361

SignNet-1M: Large-Scale Multilingual Sign Language Video Dataset with Downstream Benchmarks

Zhewen He·Junyi Hu·Haomian Huang·Zhenhua Li·Yu-Shen Liu·Yi Fang
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Abstract

Sign language models are typically trained on datasets captured under constrained conditions, with limited viewpoint, background, and signer-identity diversity, leading to poor robustness under real-world distribution shifts. We introduce SignNet-1M, a large-scale augmented dataset spanning ASL, CSL, and German Sign Language (DGS). SignNet-1M synthesizes realistic variations along three axes: (i) novel-view rendering (rotation and zoom) via 3D Gaussian Splatting (3DGS), (ii) scene/identity editing via diffusion models for background replacement and signer substitution while preserving sign motion and linguistic content, and (iii) post-rendering augmentations that emulate capture and compression artifacts (e.g., pose/temporal perturbations and video-level corruptions) to better match in-the-wild recordings. Beyond data release, we provide a unified benchmark suite across downstream tasks (e.g., translation and recognition) and ablations that isolate each augmentation component. Experiments across backbones show that training with SignNet-1M consistently improves generalization under cross-view, cross-background, cross-identity, and post-rendering shifts, while maintaining strong in-distribution performance. The dataset, full augmentation pipeline, and benchmark are available at https://signnet.chatsign.ai/.

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Zhewen He, Junyi Hu, Haomian Huang, Zhenhua Li, Yu-Shen Liu, Yi Fang. 2026-06-23. SignNet-1M: Large-Scale Multilingual Sign Language Video Dataset with Downstream Benchmarks. https://arxiv.org/abs/2606.24361

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