Text-Driven Diffusion Model for Sign Language Production
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909545378349056 |
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| author | He, Jiayi Wang, Xu Zhang, Ruobei Tang, Shengeng Wang, Yaxiong Cheng, Lechao |
| author_facet | He, Jiayi Wang, Xu Zhang, Ruobei Tang, Shengeng Wang, Yaxiong Cheng, Lechao |
| contents | We introduce the hfut-lmc team's solution to the SLRTP Sign Production Challenge. The challenge aims to generate semantically aligned sign language pose sequences from text inputs. To this end, we propose a Text-driven Diffusion Model (TDM) framework. During the training phase, TDM utilizes an encoder to encode text sequences and incorporates them into the diffusion model as conditional input to generate sign pose sequences. To guarantee the high quality and accuracy of the generated pose sequences, we utilize two key loss functions. The joint loss function L_{joint} is used to precisely measure and minimize the differences between the joint positions of the generated pose sequences and those of the ground truth. Similarly, the bone orientation loss function L_{bone} is instrumental in ensuring that the orientation of the bones in the generated poses aligns with the actual, correct orientations. In the inference stage, the TDM framework takes on a different yet equally important task. It starts with noisy sequences and, under the strict constraints of the text conditions, gradually refines and generates semantically consistent sign language pose sequences. Our carefully designed framework performs well on the sign language production task, and our solution achieves a BLEU-1 score of 20.17, placing second in the challenge. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_15914 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Text-Driven Diffusion Model for Sign Language Production He, Jiayi Wang, Xu Zhang, Ruobei Tang, Shengeng Wang, Yaxiong Cheng, Lechao Computer Vision and Pattern Recognition We introduce the hfut-lmc team's solution to the SLRTP Sign Production Challenge. The challenge aims to generate semantically aligned sign language pose sequences from text inputs. To this end, we propose a Text-driven Diffusion Model (TDM) framework. During the training phase, TDM utilizes an encoder to encode text sequences and incorporates them into the diffusion model as conditional input to generate sign pose sequences. To guarantee the high quality and accuracy of the generated pose sequences, we utilize two key loss functions. The joint loss function L_{joint} is used to precisely measure and minimize the differences between the joint positions of the generated pose sequences and those of the ground truth. Similarly, the bone orientation loss function L_{bone} is instrumental in ensuring that the orientation of the bones in the generated poses aligns with the actual, correct orientations. In the inference stage, the TDM framework takes on a different yet equally important task. It starts with noisy sequences and, under the strict constraints of the text conditions, gradually refines and generates semantically consistent sign language pose sequences. Our carefully designed framework performs well on the sign language production task, and our solution achieves a BLEU-1 score of 20.17, placing second in the challenge. |
| title | Text-Driven Diffusion Model for Sign Language Production |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.15914 |