Prediction of human actions in social interactions has important applications
in the design of social robots or artificial avatars. In this paper, we focus
on a unimodal representation of interactions and propose to tackle interaction
generation in a data-driven fashion. In particular, we model human interaction
generation as a discrete multi-sequence generation problem and present
SocialInteractionGAN, a novel adversarial architecture for conditional
interaction generation. Our model builds on a recurrent encoder-decoder
generator network and a dual-stream discriminator, that jointly evaluates the
realism of interactions and individual action sequences and operates at
different time scales. Crucially, contextual information on interacting
participants is shared among agents and reinjected in both the generation and
the discriminator evaluation processes. Experiments show that albeit dealing
with low dimensional data, SocialInteractionGAN succeeds in producing high
realism action sequences of interacting people, comparing favorably to a
diversity of recurrent and convolutional discriminator baselines, and we argue
that this work will constitute a first stone towards higher dimensional and
multimodal interaction generation. Evaluations are conducted using classical
GAN metrics, that we specifically adapt for discrete sequential data. Our model
is shown to properly learn the dynamics of interaction sequences, while
exploiting the full range of available actions.

本文介绍了 SocialInteractionGAN—— 一种新颖的基于数据驱动的对抗性生成网络框架，其旨在以单模态展示的相互作用为重点，以离散多序列生成问题为基础，以建立在递归编码器 - 解码器生成网络和双流鉴别器之上的方法来处理相互作用的生成问题。实验结果表明，SocialInteractionGAN 能成功地生成以人为中心的高真实感互动动作序列，这对社交机器人或人工头像的设计具有重要应用。

SocialInteractionGAN: 多人互动序列生成

SocialInteractionGAN: Multi-person Interaction Sequence Generation

Object segmentation is a crucial problem that is usually solved by using
supervised learning approaches over very large datasets composed of both images
and corresponding object masks. Since the masks have to be provided at pixel
level, building such a dataset for any new domain can be very time-consuming.
We present ReDO, a new model able to extract objects from images without any
annotation in an unsupervised way. It relies on the idea that it should be
possible to change the textures or colors of the objects without changing the
overall distribution of the dataset. Following this assumption, our approach is
based on an adversarial architecture where the generator is guided by an input
sample: given an image, it extracts the object mask, then redraws a new object
at the same location. The generator is controlled by a discriminator that
ensures that the distribution of generated images is aligned to the original
one. We experiment with this method on different datasets and demonstrate the
good quality of extracted masks.

ReDO 是一个基于对抗架构的无监督学习模型，能够从图像中提取物体掩码，无需任何注释。实验结果表明，该方法在不同数据集上提取的掩码具有良好质量。