This paper presents a novel approach to generating the 3D motion of a human
interacting with a target object, with a focus on solving the challenge of
synthesizing long-range and diverse motions, which could not be fulfilled by
existing auto-regressive models or path planning-based methods. We propose a
hierarchical generation framework to solve this challenge. Specifically, our
framework first generates a set of milestones and then synthesizes the motion
along them. Therefore, the long-range motion generation could be reduced to
synthesizing several short motion sequences guided by milestones. The
experiments on the NSM, COUCH, and SAMP datasets show that our approach
outperforms previous methods by a large margin in both quality and diversity.
The source code is available on our project page
this https URL

提出了一种新方法，通过 hieraechical generation framework 来生成 3D 运动，以解决生成多样且长距离运动的挑战，并且在 NSM、COUCH 和 SAMP 数据集上的实验证明该方法在质量和多样性方面优于以前的方法。

基于扩散概率模型的人 - 物交互的分层生成

Hierarchical Generation of Human-Object Interactions with Diffusion  Probabilistic Models

Automatic storytelling is challenging since it requires generating long,
coherent natural language to describes a sensible sequence of events. Despite
considerable efforts on automatic story generation in the past, prior work
either is restricted in plot planning, or can only generate stories in a narrow
domain. In this paper, we explore open-domain story generation that writes
stories given a title (topic) as input. We propose a plan-and-write
hierarchical generation framework that first plans a storyline, and then
generates a story based on the storyline. We compare two planning strategies.
The dynamic schema interweaves story planning and its surface realization in
text, while the static schema plans out the entire storyline before generating
stories. Experiments show that with explicit storyline planning, the generated
stories are more diverse, coherent, and on topic than those generated without
creating a full plan, according to both automatic and human evaluations.

本文探讨了根据标题生成故事的开放域故事生成，提出了一种计划和写作的分层式生成框架，比较了两种规划策略，并通过实验表明了明确的故事情节规划会让生成的故事更加多样化、连贯且主题贴切。