Head generation with diverse identities is an important task in computer vision and computer graphics, widely used in multimedia applications.
1 code implementation • • Benjamin Wilson, William Qi, Tanmay Agarwal, John Lambert, Jagjeet Singh, Siddhesh Khandelwal, Bowen Pan, Ratnesh Kumar, Andrew Hartnett, Jhony Kaesemodel Pontes, Deva Ramanan, Peter Carr, James Hays
Models are tasked with the prediction of future motion for "scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category.
In 3D face reconstruction, orthogonal projection has been widely employed to substitute perspective projection to simplify the fitting process.
The self-attention-based model, transformer, is recently becoming the leading backbone in the field of computer vision.
Specifically, the proposed deep semi-supervised AU recognition approach consists of a deep recognition network and a discriminator D. The deep recognition network R learns facial representations from large-scale facial images and AU classifiers from limited ground truth AU labels.
Second, to effectively transfer knowledge, we develop a dynamic block swapping method by randomly replacing the blocks in the lower-precision student network with the corresponding blocks in the higher-precision teacher network.
An inherent property of real-world videos is the high correlation of information across frames which can translate into redundancy in either temporal or spatial feature maps of the models, or both.
Videos capture events that typically contain multiple sequential, and simultaneous, actions even in the span of only a few seconds.
Our further experiment on a LoCoBot robot shows that our model enables the surrounding sensing capability from 2D image input.
Deep convolutional neural networks (CNNs) have made impressive progress in many video recognition tasks such as video pose estimation and video object detection.