# SLRTP2025 Sign Language Production Challenge: Methodology, Results, and Future Work arXiv: https://arxiv.org/abs/2508.06951 ยท HTML: https://mlsanity.com/paper/2508.06951/ ## Key facts - Cited by 4 papers in the map (data through 14 Oct 2026). - Builds on 30 of its 56 references that are in the map. - First posted on arXiv 9 Aug 2025. ## Builds on (most cited first, 30 of 30) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020) - [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020) - [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019) - [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018) - [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016) - [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019) - [Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation](https://mlsanity.com/paper/2003.13830/) (2020) - [Two-Stream Network for Sign Language Recognition and Translation](https://mlsanity.com/paper/2211.01367/) (2022) - [Progressive Transformers for End-to-End Sign Language Production](https://mlsanity.com/paper/2004.14874/) (2020) - [Codabench: Flexible, Easy-to-Use and Reproducible Benchmarking Platform](https://mlsanity.com/paper/2110.05802/) (2021) - [Signing at Scale: Learning to Co-Articulate Signs for Large-Scale Photo-Realistic Sign Language Production](https://mlsanity.com/paper/2203.15354/) (2022) - [Adversarial Training for Multi-Channel Sign Language Production](https://mlsanity.com/paper/2008.12405/) (2020) - [Mixed SIGNals: Sign Language Production via a Mixture of Motion Primitives](https://mlsanity.com/paper/2107.11317/) (2021) - [Neural Sign Actors: A diffusion model for 3D sign language production from text](https://mlsanity.com/paper/2312.02702/) (2023) - [Everybody Sign Now: Translating Spoken Language to Photo Realistic Sign Language Video](https://mlsanity.com/paper/2011.09846/) (2020) - [SignDiff: Diffusion Model for American Sign Language Production](https://mlsanity.com/paper/2308.16082/) (2023) - [A Simple Baseline for Spoken Language to Sign Language Translation with 3D Avatars](https://mlsanity.com/paper/2401.04730/) (2024) - [A Data-Driven Representation for Sign Language Production](https://mlsanity.com/paper/2404.11499/) (2024) - [Improving 3D Pose Estimation for Sign Language](https://mlsanity.com/paper/2308.09525/) (2023) - [Discrete to Continuous: Generating Smooth Transition Poses from Sign Language Observation](https://mlsanity.com/paper/2411.16810/) (2024) - [Changing the Representation: Examining Language Representation for Neural Sign Language Production](https://mlsanity.com/paper/2210.06312/) (2022) - [Sign Language Production with Latent Motion Transformer](https://mlsanity.com/paper/2312.12917/) (2023) - [Giving a Hand to Diffusion Models: a Two-Stage Approach to Improving Conditional Human Image Generation](https://mlsanity.com/paper/2403.10731/) (2024) - [Autoregressive Sign Language Production: A Gloss-Free Approach with Discrete Representations](https://mlsanity.com/paper/2309.12179/) (2023) - [Sign Stitching: A Novel Approach to Sign Language Production](https://mlsanity.com/paper/2405.07663/) (2024) - [Disentangle and Regularize: Sign Language Production with Articulator-Based Disentanglement and Channel-Aware Regularization](https://mlsanity.com/paper/2504.06610/) (2025) - [Unsupervised Sign Language Translation and Generation](https://mlsanity.com/paper/2402.07726/) (2024) - [Select and Reorder: A Novel Approach for Neural Sign Language Production](https://mlsanity.com/paper/2404.11532/) (2024) - [Text-Driven Diffusion Model for Sign Language Production](https://mlsanity.com/paper/2503.15914/) (2025) ## Built on by (most cited first, 4 of 4) - [A2V-SLP: Alignment-Aware Variational Modeling for Disentangled Sign Language Production](https://mlsanity.com/paper/2602.11861/) (2026) - [Towards Continuous Sign Language Conversation from Isolated Signs](https://mlsanity.com/paper/2605.14705/) (2026) - [The Impact of VAE Design on Latent Pose Representations for Diffusion-based Sign Language Production](https://mlsanity.com/paper/2606.22959/) (2026) - [SignRR: Retrieve and Refine Real Motion for Sign Language Production](https://mlsanity.com/paper/2608.28568/) (2026) ## Abstract (arXiv) Sign Language Production (SLP) is the task of generating sign language video from spoken language inputs. The field has seen a range of innovations over the last few years, with the introduction of deep learning-based approaches providing significant improvements in the realism and naturalness of generated outputs. However, the lack of standardized evaluation metrics for SLP approaches hampers meaningful comparisons across different systems. To address this, we introduce the first Sign Language Production Challenge, held as part of the third SLRTP Workshop at CVPR 2025. The competition's aims are to evaluate architectures that translate from spoken language sentences to a sequence of skeleton poses, known as Text-to-Pose (T2P) translation, over a range of metrics. For our evaluation data, we use the RWTH-PHOENIX-Weather-2014T dataset, a German Sign Language - Deutsche Gebardensprache (DGS) weather broadcast dataset. In addition, we curate a custom hidden test set from a similar domain of discourse. This paper presents the challenge design and the winning methodologies. The challenge attracted 33 participants who submitted 231 solutions, with the top-performing team achieving BLEU-1 scores of 31.40 and DTW-MJE of 0.0574. The winning approach utilized a retrieval-based framework and a pre-trained language model. As part of the workshop, we release a standardized evaluation network, including high-quality skeleton extraction-based keypoints establishing a consistent baseline for the SLP field, which will enable future researchers to compare their work against a broader range of methods.