直接生成连续坐标姿态，避免误差累积，提升长时序动作一致性。
Towards Consistent Long-Term Pose Generation
- 一阶段直接输出连续坐标，用相对运动预测保持空间关系。
- 在彭尼动作和第一人称手部动作数据集上，长时生成效果显著超越基线。
- 无需中间表示或分步生成，适合追求高一致性的长期动作合成任务。
当前姿态生成方法严重依赖中间表示，要么采用量化两阶段流程，要么使用自回归模型在推理中累积误差。这一根本局限导致性能下降，尤其在需要维持时间连贯性的长时姿态生成中更为明显。我们提出一种新型一阶段架构，仅需单张RGB图像和文本描述，即可在连续坐标空间中直接生成姿态，并确保训练与推理阶段分布一致。核心创新在于：通过相对运动预测机制直接操作姿态坐标，保持空间关系；采用统一占位符标记法，实现单次前向传播，且训练与推理行为完全相同。在Penn Action和First-Person Hand Action Benchmark（F-PHAB）数据集上的大量实验表明，该方法显著优于现有的量化与自回归方法，尤其在长时生成场景下表现突出。
原文摘要 · Abstract (English)
Current approaches to pose generation rely heavily on intermediate representations, either through two-stage pipelines with quantization or autoregressive models that accumulate errors during inference. This fundamental limitation leads to degraded performance, particularly in long-term pose generation where maintaining temporal coherence is crucial. We propose a novel one-stage architecture that directly generates poses in continuous coordinate space from minimal context - a single RGB image and text description - while maintaining consistent distributions between training and inference. Our key innovation is eliminating the need for intermediate representations or token-based generation by operating directly on pose coordinates through a relative movement prediction mechanism that preserves spatial relationships, and a unified placeholder token approach that enables single-forward generation with identical behavior during training and inference. Through extensive experiments on Penn Action and First-Person Hand Action Benchmark (F-PHAB) datasets, we demonstrate that our approach significantly outperforms existing quantization-based and autoregressive methods, especially in long-term generation scenarios.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。