We propose a neural talking-head video synthesis model and demonstrate its
application to video conferencing. Our model learns to synthesize a
talking-head video using a source image containing the target person's
appearance and a driving video that dictates the motion in the output. Our
motion is encoded based on a novel keypoint representation, where the
identity-specific and motion-related information is decomposed unsupervisedly.
Extensive experimental validation shows that our model outperforms competing
methods on benchmark datasets. Moreover, our compact keypoint representation
enables a video conferencing system that achieves the same visual quality as
the commercial H.264 standard while only using one-tenth of the bandwidth.
Besides, we show our keypoint representation allows the user to rotate the head
during synthesis, which is useful for simulating face-to-face video
conferencing experiences.

提出了一种新颖的神经网络生成模型，采用关键点作为运动信息的表征形式，从而实现了面部图像的生成，降低了视频会议的带宽要求。

视频会议用单镜头自由视角神经对话人头合成

One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing

We aim to develop an algorithm for robots to manipulate novel objects as
tools for completing different task goals. An efficient and informative
representation would facilitate the effectiveness and generalization of such
algorithms. For this purpose, we present KETO, a framework of learning keypoint
representations of tool-based manipulation. For each task, a set of
task-specific keypoints is jointly predicted from 3D point clouds of the tool
object by a deep neural network. These keypoints offer a concise and
informative description of the object to determine grasps and subsequent
manipulation actions. The model is learned from self-supervised robot
interactions in the task environment without the need for explicit human
annotations. We evaluate our framework in three manipulation tasks with tool
use. Our model consistently outperforms state-of-the-art methods in terms of
task success rates. Qualitative results of keypoint prediction and tool
generation are shown to visualize the learned representations.

本文基于深度学习，提出了一种名为 KETO 的自监督学习框架，对于机器人使用工具进行操作具有很好的表现，其使用任务特定的关键点表示工具，提高了其操作的有效性和泛化性能，结果表明该模型在三个工具使用的操作任务中表现均优于当前最先进的方法