**LipGAN** is a generative adversarial network for generating realistic talking faces conditioned on translated speech. It employs an adversary that measures the extent of lip synchronization in the frames generated by the generator. The system is capable of handling faces in random poses without the need for realignment to a template pose. LipGAN is a fully self-supervised approach that learns a phoneme-viseme mapping, making it language independent.

Computer vision is an interesting tool for animal behavior monitoring, mainly because it limits animal handling and it can be used to record various traits using only one sensor. From previous studies, this technic has shown to be suitable for various species and behavior. However it remains challenging to collect individual information, i.e. not only to detect animals and behavior on the video frames, but also to identify them. Animal identification is a prerequisite to gather individual information in order to characterize individuals and compare them. A common solution to this problem, known as multiple objects tracking, consists in detecting the animals on each video frame, and then associate detections to a unique animal ID. Association of detections between two consecutive frames are generally made to maintain coherence of the detection locations and appearances. To extract appearance information, a common solution is to use a convolutional neural network (CNN), trained on a large dataset before running the tracking algorithm. For farmed animals, designing such network is challenging as far as large training dataset are still lacking. In this article, we proposed an innovative solution, where the CNN used to extract appearance information is parameterized using offline unsupervised training. The algorithm, named Wizard, was evaluated for the purpose of goats monitoring in outdoor conditions. 17 annotated videos were used, for a total of 4H30, with various number of animals on the video (from 3 to 8) and different level of color differences between animals. First, the ability of the algorithm to track the detected animals was evaluated. When animals were detected, the algorithm found the correct animal ID in 94.82% of the frames. When tracking and detection were evaluated together, we found that Wizard found the correct animal ID in 86.18% of the video length. In situations where the animal detection rate could be high, Wizard seems to be a suitable solution for individual behavior analysis experiments based on computer vision.

Wizard

LipGAN

Towards Automatic Face-to-Face Translation

**Implicit PointRend** is a modification to the [PointRend](https://paperswithcode.com/method/pointrend) module for instance segmentation. Instead of a coarse mask prediction used in [PointRend](https://paperswithcode.com/method/pointrend) to provide region-level context to distinguish objects, for each object Implicit PointRend generates different parameters for a function that makes the final pointwise mask prediction. The new model is more straightforward than PointRend: (1) it does not require an importance point sampling during training and (2) it uses a single point-level mask loss instead of two mask losses. Implicit PointRend can be trained directly with point supervision without any intermediate prediction interpolation steps.

Source	Towards Automatic Face-to-Face Translation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com