Human activity videos involve rich, varied interactions between people and
objects. In this paper we develop methods for generating such videos -- making
progress toward addressing the important, open problem of video generation in
complex scenes. In particular, we introduce the task of generating human-object
interaction videos in a zero-shot compositional setting, i.e., generating
videos for action-object compositions that are unseen during training, having
seen the target action and target object separately. This setting is
particularly important for generalization in human activity video generation,
obviating the need to observe every possible action-object combination in
training and thus avoiding the combinatorial explosion involved in modeling
complex scenes. To generate human-object interaction videos, we propose a novel
adversarial framework HOI-GAN which includes multiple discriminators focusing
on different aspects of a video. To demonstrate the effectiveness of our
proposed framework, we perform extensive quantitative and qualitative
evaluation on two challenging datasets: EPIC-Kitchens and
20BN-Something-Something v2.

本研究提出了一种零样本组合条件下生成人物 - 物品交互视频的方法，并使用一种新型对抗框架 HOI-GAN 进行探索和验证，在两个具有挑战性的数据集：EPIC-Kitchens 和 20BN-Something-Something v2 上进行了广泛的定量和定性评估。