Then, we introduce a transformer-based fusion module that integrates the static vision features and the dynamic multimodal features.
Kernel density estimation is arguably one of the most commonly used density estimation techniques, and the use of "sliding window" mechanism adapts kernel density estimators to dynamic processes.
Hence, we propose a novel one-shot talking face generation framework by exploring consistent correlations between audio and visual motions from a specific speaker and then transferring audio-driven motion fields to a reference image.
An autonomous experimentation platform in manufacturing is supposedly capable of conducting a sequential search for finding suitable manufacturing conditions for advanced materials by itself or even for discovering new materials with minimal human intervention.
It is worth to note that our proposed RAA convolution is lightweight and compatible to be integrated into any CNN architecture used for the BEV detection.
As this keypoint based representation models the motions of facial regions, head, and backgrounds integrally, our method can better constrain the spatial and temporal consistency of the generated videos.
Automatic affective recognition has been an important research topic in human computer interaction (HCI) area.
To synthesize high-definition videos, we build a large in-the-wild high-resolution audio-visual dataset and propose a novel flow-guided talking face generation framework.
The facial expression analysis requires a compact and identity-ignored expression representation.
In this work, we propose a novel carefully designed deep learning framework, named deep motion interpolation network (DMIN), to learn human movement habits from a real dataset and then to perform the interpolation function specific for human motions.
To the best of our knowledge, this is the first work to evaluate the cine MRI with deep learning reconstruction for cardiac function analysis and compare it with other conventional methods.
To be specific, our framework consists of a speaker-independent stage and a speaker-specific stage.
Using existing model selection methods, like cross validation, results in model overfitting in presence of temporal autocorrelation.
Advances in deep learning enable us to perform image-to-image transformation tasks for various types of microscopy image reconstruction, computationally producing high-quality images from the physically acquired low-quality ones.
The proposed approach is developed as an effort to address a data association challenge in which the number of vessels as well as the vessel identification are purposely withheld and time gaps are created in the datasets to mimic the real-life operational complexities under a threat environment.
In order to make dimensionality reduction effective for high-dimensional data embedding nonlinear low-dimensional manifold, it is understood that some sort of geodesic distance metric should be used to discriminate the data samples.
The proposed method consists of the data preprocessing, the feature extraction and the AU classification.
Dimensionality reduction is considered as an important step for ensuring competitive performance in unsupervised learning such as anomaly detection.
The first difference is that in the electron imaging setting, we have a pair of physical high-resolution and low-resolution images, rather than a physical image with its downsampled counterpart.
Image and Video Processing
This paper presents a novel multi-identity face reenactment framework, named FReeNet, to transfer facial expressions from an arbitrary source face to a target face with a shared model.
Self-organizing map(SOM) have been widely applied in clustering, this paper focused on centroids of clusters and what they reveal.