Chunyu Sui

Sen Fang, Chunyu Sui, Yanghao Zhou et al.

In this paper, we propose a dual-condition diffusion pre-training model named SignDiff that can generate human sign language speakers from a skeleton pose. SignDiff has a novel Frame Reinforcement Network called FR-Net, similar to dense human pose estimation work, which enhances the correspondence between text lexical symbols and sign language dense pose frames, reduces the occurrence of multiple fingers in the diffusion model. In addition, we propose a new method for American Sign Language Production (ASLP), which can generate ASL skeletal pose videos from text input, integrating two new improved modules and a new loss function to improve the accuracy and quality of sign language skeletal posture and enhance the ability of the model to train on large-scale data. We propose the first baseline for ASL production and report the scores of 17.19 and 12.85 on BLEU-4 on the How2Sign dev/test sets. We evaluated our model on the previous mainstream dataset PHOENIX14T, and the experiments achieved the SOTA results. In addition, our image quality far exceeds all previous results by 10 percentage points in terms of SSIM.

Sen Fang, Chen Chen, Lei Wang et al.

In this paper, we propose SignLLM, a multilingual Sign Language Production (SLP) large language model, which includes two novel multilingual SLP modes MLSF and Prompt2LangGloss that allow sign language gestures generation from query texts input and question-style prompts input respectively. Both modes can use a new RL loss based on reinforcement learning and a new RL module named Priority Learning Channel. These RL components can accelerate the training by enhancing the model's capability to sample high-quality data. To train SignLLM, we introduce Prompt2Sign, a comprehensive multilingual sign language dataset, which builds from public data, including American Sign Language (ASL) and seven others. This dataset standardizes information by extracting pose information from sign language videos into a unified compressed format. We extensively evaluate SignLLM, demonstrating that our model achieves state-of-the-art performance on SLP tasks across eight sign languages.

Sen Fang, Chunyu Sui, Hongwei Yi et al.

The complexity of sign language data processing brings many challenges. The current approach to recognition of ASL signs aims to translate RGB sign language videos through pose information into English-based ID glosses, which serve to uniquely identify ASL signs. Note that there is no shared convention for assigning such glosses to ASL signs, so it is essential that the same glossing conventions are used for all of the data in the datasets that are employed. This paper proposes SignX, a foundation model framework for sign recognition. It is a concise yet powerful framework applicable to multiple human activity recognition scenarios. First, we developed a Pose2Gloss component based on an inverse diffusion model, which contains a multi-track pose fusion layer that unifies five of the most powerful pose information sources--SMPLer-X, DWPose, Mediapipe, PrimeDepth, and Sapiens Segmentation--into a single latent pose representation. Second, we trained a Video2Pose module based on ViT that can directly convert raw video into signer pose representation. Through this 2-stage training framework, we enable sign language recognition models to be compatible with existing pose formats, laying the foundation for the common pose estimation necessary for sign recognition. Experimental results show that SignX can recognize signs from sign language video, producing predicted gloss representations with greater accuracy than has been reported in prior work.