We propose a novel framework, On-Demand MOtion Generation (ODMO), for generating realistic and diverse long-term 3D human motion sequences conditioned only on action types with an additional capability of customization. ODMO shows improvements over SOTA approaches on all traditional motion evaluation metrics when evaluated on three public datasets (HumanAct12, UESTC, and MoCap). Furthermore, we provide both qualitative evaluations and quantitative metrics demonstrating several first-known customization capabilities afforded by our framework, including mode discovery, interpolation, and trajectory customization. These capabilities significantly widen the spectrum of potential applications of such motion generation models. The novel on-demand generative capabilities are enabled by innovations in both the encoder and decoder architectures: (i) Encoder: Utilizing contrastive learning in low-dimensional latent space to create a hierarchical embedding of motion sequences, where not only the codes of different action types form different groups, but within an action type, codes of similar inherent patterns (motion styles) cluster together, making them readily discoverable; (ii) Decoder: Using a hierarchical decoding strategy where the motion trajectory is reconstructed first and then used to reconstruct the whole motion sequence. Such an architecture enables effective trajectory control. Our code is released on the Github page: https://github.com/roychowdhuryresearch/ODMO
翻译:我们提出了一个新颖的框架,即 " 需求移动生成 " (ODMO),以产生现实和多样化的长期3D人类运动序列,仅以行动类型为条件,并具有额外的定制能力。ODMO展示了在三个公共数据集(HumanAct12、UESTC和MoCap)上评价所有传统运动评价指标时,SOTA方法在SO评价所有传统运动评价指标方面的改进。此外,我们提供质量评价和数量指标,以展示我们框架提供的几种最先知道的定制能力,包括模式发现、内插和轨迹定制。这些能力大大扩大了这种运动生成模型的潜在应用范围。新的点播发型基因化能力是由编码和解码结构的创新所促成的:(一) 编码:利用低维潜在空间的对比学习来建立运动序列的分级嵌嵌套,其中不仅有不同行动类型代码的形式不同,而且在行动类型、相似固有模式(感官风格)的组合内,使它们易于发现。 (二) 解码:使用等级分解战略,使运动/解码能够重建整个轨道。