# SignAvatars: A Large-scale 3D Sign Language Holistic Motion Dataset and Benchmark

arXiv: https://arxiv.org/abs/2310.20436 · HTML: https://mlsanity.com/paper/2310.20436/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 65 references that are in the map.
- First posted on arXiv 31 Oct 2023.

## Builds on (most cited first, 30 of 39)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [Human Motion Diffusion Model](https://mlsanity.com/paper/2209.14916/) (2022)
- [T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations](https://mlsanity.com/paper/2301.06052/) (2023)
- [ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation](https://mlsanity.com/paper/2204.12484/) (2022)
- [Learning joint reconstruction of hands and manipulated objects](https://mlsanity.com/paper/1904.05767/) (2019)
- [PARE: Part Attention Regressor for 3D Human Body Estimation](https://mlsanity.com/paper/2104.08527/) (2021)
- [ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation](https://mlsanity.com/paper/2204.13662/) (2022)
- [Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies](https://mlsanity.com/paper/1801.01615/) (2018)
- [Motion-X: A Large-scale 3D Expressive Whole-body Human Motion Dataset](https://mlsanity.com/paper/2307.00818/) (2023)
- [Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison](https://mlsanity.com/paper/1910.11006/) (2019)
- [Generating Holistic 3D Human Motion from Speech](https://mlsanity.com/paper/2212.04420/) (2022)
- [Whole-Body Human Pose Estimation in the Wild](https://mlsanity.com/paper/2007.11858/) (2020)
- [How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language](https://mlsanity.com/paper/2008.08143/) (2020)
- [FrankMocap: A Monocular 3D Whole-Body Pose Estimation System via Regression and Integration](https://mlsanity.com/paper/2108.06428/) (2021)
- [PyMAF-X: Towards Well-aligned Full-body Model Regression from Monocular Images](https://mlsanity.com/paper/2207.06400/) (2022)
- [How2: A Large-scale Dataset for Multimodal Language Understanding](https://mlsanity.com/paper/1811.00347/) (2018)
- [Collaborative Regression of Expressive Bodies using Moderation](https://mlsanity.com/paper/2105.05301/) (2021)
- [One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer](https://mlsanity.com/paper/2303.16160/) (2023)
- [Progressive Transformers for End-to-End Sign Language Production](https://mlsanity.com/paper/2004.14874/) (2020)
- [MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language](https://mlsanity.com/paper/1812.01053/) (2018)
- [BSL-1K: Scaling up co-articulated sign language recognition using mouthing cues](https://mlsanity.com/paper/2007.12131/) (2020)
- [Weakly Supervised 3D Hand Pose Estimation via Biomechanical Constraints](https://mlsanity.com/paper/2003.09282/) (2020)
- [Video-based Sign Language Recognition without Temporal Segmentation](https://mlsanity.com/paper/1801.10111/) (2018)
- [Accurate 3D Hand Pose Estimation for Whole-Body 3D Human Mesh Estimation](https://mlsanity.com/paper/2011.11534/) (2020)
- [AUTSL: A Large Scale Multi-modal Turkish Sign Language Dataset and Baseline Methods](https://mlsanity.com/paper/2008.00932/) (2020)
- [Real-time Facial Surface Geometry from Monocular Video on Mobile GPUs](https://mlsanity.com/paper/1907.06724/) (2019)
- [Detailed Human Shape Estimation from a Single Image by Hierarchical Mesh Deformation](https://mlsanity.com/paper/1904.10506/) (2019)
- [SignBERT+: Hand-model-aware Self-supervised Pre-training for Sign Language Understanding](https://mlsanity.com/paper/2305.04868/) (2023)

## Built on by (most cited first, 29 of 29)

- [Dyn-HaMR: Recovering 4D Interacting Hand Motion from a Dynamic Camera](https://mlsanity.com/paper/2412.12861/) (2024)
- [GigaHands: A Massive Annotated Dataset of Bimanual Hand Activities](https://mlsanity.com/paper/2412.04244/) (2024)
- [SMPLest-X: Ultimate Scaling for Expressive Human Pose and Shape Estimation](https://mlsanity.com/paper/2501.09782/) (2025)
- [Signs as Tokens: A Retrieval-Enhanced Multilingual Sign Language Generator](https://mlsanity.com/paper/2411.17799/) (2024)
- [Meaningful Pose-Based Sign Language Evaluation](https://mlsanity.com/paper/2510.07453/) (2025)
- [Conditional GAN for Enhancing Diffusion Models in Efficient and Authentic Global Gesture Generation from Audios](https://mlsanity.com/paper/2410.20359/) (2024)
- [Text-Driven 3D Hand Motion Generation from Sign Language Data](https://mlsanity.com/paper/2508.15902/) (2025)
- [MaDiS: Taming Masked Diffusion Language Models for Sign Language Generation](https://mlsanity.com/paper/2601.19577/) (2026)
- [DIDiffGes: Decoupled Semi-Implicit Diffusion Models for Real-time Gesture Generation from Speech](https://mlsanity.com/paper/2503.17059/) (2025)
- [PUMPS: Skeleton-Agnostic Point-based Universal Motion Pre-Training for Synthesis in Human Motion Tasks](https://mlsanity.com/paper/2507.20170/) (2025)
- [ExpGest: Expressive Speaker Generation Using Diffusion Model and Hybrid Audio-Text Guidance](https://mlsanity.com/paper/2410.09396/) (2024)
- [RopeTP: Global Human Motion Recovery via Integrating Robust Pose Estimation with Diffusion Trajectory Prior](https://mlsanity.com/paper/2410.20358/) (2024)
- [Generative Modeling of Shape-Dependent Self-Contact Human Poses](https://mlsanity.com/paper/2509.23393/) (2025)
- [SignSparK: Efficient Multilingual Sign Language Production via Sparse Keyframe Learning](https://mlsanity.com/paper/2603.10446/) (2026)
- [SIGNER: Temporally Grounded Sign Language Generation via Time-Resolved Conditioning](https://mlsanity.com/paper/2506.07460/) (2025)
- [Leveraging Large Language Models for Accurate Sign Language Translation in Low-Resource Scenarios](https://mlsanity.com/paper/2508.18183/) (2025)
- [VividAnimator: An End-to-End Audio and Pose-driven Half-Body Human Animation Framework](https://mlsanity.com/paper/2510.10269/) (2025)
- [Large Sign Language Models: Toward 3D American Sign Language Translation](https://mlsanity.com/paper/2511.08535/) (2025)
- [DexAvatar: 3D Sign Language Reconstruction with Hand and Body Pose Priors](https://mlsanity.com/paper/2512.21054/) (2025)
- [Context-Aware Autoregressive Diffusion for Gloss-Wise Sign Language Production](https://mlsanity.com/paper/2606.21234/) (2026)
- [MAMMA: Markerless & Automatic Multi-Person Motion Action Capture](https://mlsanity.com/paper/2506.13040/) (2025)
- [OpenFS: Multi-Hand-Capable Fingerspelling Recognition with Implicit Signing-Hand Detection and Frame-Wise Letter-Conditioned Synthesis](https://mlsanity.com/paper/2602.22949/) (2026)
- [KAN Text to Vision? The Exploration of Kolmogorov-Arnold Networks for Multi-Scale Sequence-Based Pose Animation from Sign Language Notation](https://mlsanity.com/paper/2605.09572/) (2026)
- [Towards Continuous Sign Language Conversation from Isolated Signs](https://mlsanity.com/paper/2605.14705/) (2026)
- [Q-BridgeNet: A Quantization Network for Cross-Lingual Sign Language Translation](https://mlsanity.com/paper/2607.11215/) (2026)
- [Beyond BLEU: A Case for Redefining Sign Language Translation Benchmarks](https://mlsanity.com/paper/2609.03734/) (2026)
- [SignRefine: Adapting Foundational Video Models for Sign Language Generation](https://mlsanity.com/paper/2609.08496/) (2026)
- [PHOSA: Photorealistic 3D Sign Avatar Modeling and Benchmark](https://mlsanity.com/paper/2609.29292/) (2026)
- [RoboSTAR: Next-Scale Autoregressive Sign Language Translation for Humanoid Robots](https://mlsanity.com/paper/2609.32250/) (2026)

## Abstract (arXiv)

We present SignAvatars, the first large-scale, multi-prompt 3D sign language (SL) motion dataset designed to bridge the communication gap for Deaf and hard-of-hearing individuals. While there has been an exponentially growing number of research regarding digital communication, the majority of existing communication technologies primarily cater to spoken or written languages, instead of SL, the essential communication method for Deaf and hard-of-hearing communities. Existing SL datasets, dictionaries, and sign language production (SLP) methods are typically limited to 2D as annotating 3D models and avatars for SL is usually an entirely manual and labor-intensive process conducted by SL experts, often resulting in unnatural avatars. In response to these challenges, we compile and curate the SignAvatars dataset, which comprises 70,000 videos from 153 signers, totaling 8.34 million frames, covering both isolated signs and continuous, co-articulated signs, with multiple prompts including HamNoSys, spoken language, and words. To yield 3D holistic annotations, including meshes and biomechanically-valid poses of body, hands, and face, as well as 2D and 3D keypoints, we introduce an automated annotation pipeline operating on our large corpus of SL videos. SignAvatars facilitates various tasks such as 3D sign language recognition (SLR) and the novel 3D SL production (SLP) from diverse inputs like text scripts, individual words, and HamNoSys notation. Hence, to evaluate the potential of SignAvatars, we further propose a unified benchmark of 3D SL holistic motion production. We believe that this work is a significant step forward towards bringing the digital world to the Deaf and hard-of-hearing communities as well as people interacting with them.