用扩散模型自动补全人形动作，生成自然流畅的5秒运动序列。
Human Motion Synthesis_ A Diffusion Approach for Motion Stitching and In-Betweening
- 基于Transformer的去噪器设计，实现动作片段间的平滑衔接。
- 可处理任意数量输入姿态，生成75帧（5秒，15fps）真实感动作序列。
- 适合动画、游戏等领域需要自动化动作补全的场景。
人体动作生成是多个领域的研究重点。本文针对动作拼接与中间帧生成问题，提出一种基于Transformer的去噪器扩散模型。现有方法要么依赖人工干预，要么无法处理长序列。所提方法能将任意数量的输入姿态转换为平滑且真实的动作序列，共75帧，帧率为15 fps，总时长5秒。通过弗雷切特起始距离（FID）、多样性与多模态性等量化指标，以及生成结果的视觉评估，验证了该方法在生成中间帧序列上的优异表现。
原文摘要 · Abstract (English)
Human motion generation is an important area of research in many fields. In this work, we tackle the problem of motion stitching and in-betweening. Current methods either require manual efforts, or are incapable of handling longer sequences. To address these challenges, we propose a diffusion model with a transformer-based denoiser to generate realistic human motion. Our method demonstrated strong performance in generating in-betweening sequences, transforming a variable number of input poses into smooth and realistic motion sequences consisting of 75 frames at 15 fps, resulting in a total duration of 5 seconds. We present the performance evaluation of our method using quantitative metrics such as Frechet Inception Distance (FID), Diversity, and Multimodality, along with visual assessments of the generated outputs.
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