SignPR：一种用于手语生成的渐进式矢量量化扩散框架
SignPR: A Progressive Vector-Quantized Diffusion Framework for Sign Language Production
把 VQVAE 的离散语义表征与分块因果扩散结合，先出语义一致的整体姿态再逐步细化运动细节，这套「离散 token + 渐进细化 + 因果分块」的生成范式可迁移到人体动作生成、手势驱动和长序列姿态合成等视觉任务。
原文依据 a structural VQVAE encodes each frame into semantic-aware and region-based discrete representations
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论手语生成旨在从口语生成手语序列，其中由文本生成手语姿态序列通常被视为一项重要任务。然而，由于手语姿态序列与口语文本在语法规则和模态上存在差异，在保持语义一致性、运动准确性和时序连贯性的同时将文本转换为手语姿态（即 Text2Pose）颇具挑战。本文聚焦 Text2Pose 任务，提出 SignPR，一个联合建模手语的结构属性与时序属性的渐进式扩散框架。在结构上，我们进行渐进式结构细化：结构 VQVAE 将每一帧编码为语义感知的、基于区域的离散表征；扩散过程首先生成语义一致的姿态，随后在文本与语义条件下逐步细化运动细节。在时序上，我们引入分块因果扩散，逐步施加时序连贯性约束，并支持对先前生成片段的迭代细化，从而获得更平滑的过渡并减少抖动。在广泛使用的数据集上的大量实验表明，SignPR 在多项指标上优于先前的 T2P 方法，生成的姿态序列语义忠实、运动准确且时序连贯。
英文原文摘要 1410 字符
Sign language production aims to generate sign sequences from spoken language, where the generation of sign pose sequences from text is often treated as a significant task. However, due to the differences in grammatical rules and modalities between sign language pose sequences and spoken language text, it is rather challenging to convert text into sign poses (i.e., Text2Pose), while maintaining semantic consistency, motion accuracy and temporal coherence.In this paper, we focus on the Text2Pose task, and propose SignPR, a progressive diffusion framework that jointly models the structural and temporal properties of signing. Structurally, we perform progressive structural refinement: a structural VQVAE encodes each frame into semantic-aware and region-based discrete representations; the diffusion process first produces semantically consistent poses and then progressively refines motion details under text and semantic conditioning. Temporally, we introduce block-wise causal diffusion, which progressively enforces temporal coherence and enables iterative refinement to earlier generated segments, yielding smoother transitions and reduced jitter. Extensive experiments on widely used datasets demonstrate that SignPR achieves superior results compared with prior T2P methods across multiple metrics, producing pose sequences that are semantically faithful, motion-accurate, and temporally coherent.