544 research outputs found
06241 Abstracts Collection -- Human Motion - Understanding, Modeling, Capture and Animation. 13th Workshop
From 11.06.06 to 16.06.06, the Dagstuhl Seminar 06241 ``Human Motion - Understanding, Modeling, Capture and Animation. 13th Workshop "Theoretical Foundations of Computer Vision"\u27\u27 was held
in the International Conference and Research Center (IBFI),
Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general
Object Tracking
Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application
DynamicSurf: Dynamic Neural RGB-D Surface Reconstruction with an Optimizable Feature Grid
We propose DynamicSurf, a model-free neural implicit surface reconstruction
method for high-fidelity 3D modelling of non-rigid surfaces from monocular
RGB-D video. To cope with the lack of multi-view cues in monocular sequences of
deforming surfaces, one of the most challenging settings for 3D reconstruction,
DynamicSurf exploits depth, surface normals, and RGB losses to improve
reconstruction fidelity and optimisation time. DynamicSurf learns a neural
deformation field that maps a canonical representation of the surface geometry
to the current frame. We depart from current neural non-rigid surface
reconstruction models by designing the canonical representation as a learned
feature grid which leads to faster and more accurate surface reconstruction
than competing approaches that use a single MLP. We demonstrate DynamicSurf on
public datasets and show that it can optimize sequences of varying frames with
speedup over pure MLP-based approaches while achieving comparable
results to the state-of-the-art methods. Project is available at
https://mirgahney.github.io//DynamicSurf.io/
Deepfake Video Detection Using Generative Convolutional Vision Transformer
Deepfakes have raised significant concerns due to their potential to spread
false information and compromise digital media integrity. In this work, we
propose a Generative Convolutional Vision Transformer (GenConViT) for deepfake
video detection. Our model combines ConvNeXt and Swin Transformer models for
feature extraction, and it utilizes Autoencoder and Variational Autoencoder to
learn from the latent data distribution. By learning from the visual artifacts
and latent data distribution, GenConViT achieves improved performance in
detecting a wide range of deepfake videos. The model is trained and evaluated
on DFDC, FF++, DeepfakeTIMIT, and Celeb-DF v2 datasets, achieving high
classification accuracy, F1 scores, and AUC values. The proposed GenConViT
model demonstrates robust performance in deepfake video detection, with an
average accuracy of 95.8% and an AUC value of 99.3% across the tested datasets.
Our proposed model addresses the challenge of generalizability in deepfake
detection by leveraging visual and latent features and providing an effective
solution for identifying a wide range of fake videos while preserving media
integrity. The code for GenConViT is available at
https://github.com/erprogs/GenConViT.Comment: 11 pages, 4 figure
Face Emotion Recognition Based on Machine Learning: A Review
Computers can now detect, understand, and evaluate emotions thanks to recent developments in machine learning and information fusion. Researchers across various sectors are increasingly intrigued by emotion identification, utilizing facial expressions, words, body language, and posture as means of discerning an individual's emotions. Nevertheless, the effectiveness of the first three methods may be limited, as individuals can consciously or unconsciously suppress their true feelings. This article explores various feature extraction techniques, encompassing the development of machine learning classifiers like k-nearest neighbour, naive Bayesian, support vector machine, and random forest, in accordance with the established standard for emotion recognition. The paper has three primary objectives: firstly, to offer a comprehensive overview of effective computing by outlining essential theoretical concepts; secondly, to describe in detail the state-of-the-art in emotion recognition at the moment; and thirdly, to highlight important findings and conclusions from the literature, with an emphasis on important obstacles and possible future paths, especially in the creation of state-of-the-art machine learning algorithms for the identification of emotions
Facial Micro- and Macro-Expression Spotting and Generation Methods
Facial micro-expression (ME) recognition requires face movement interval as input, but computer methods in spotting ME are still underperformed. This is due to lacking large-scale long video dataset and ME generation methods are in their infancy. This thesis presents methods to address data deficiency issues and introduces a new method for spotting macro- and micro-expressions simultaneously.
This thesis introduces SAMM Long Videos (SAMM-LV), which contains 147 annotated long videos, and develops a baseline method to facilitate ME Grand Challenge 2020. Further, a reference-guided style transfer of StarGANv2 is experimented on SAMM-LV to generate a synthetic dataset, namely SAMM-SYNTH. The quality of SAMM-SYNTH is evaluated by using facial action units detected by OpenFace. Quantitative measurement shows high correlations on two Action Units (AU12 and AU6) of the original and synthetic data.
In facial expression spotting, a two-stream 3D-Convolutional Neural Network with temporal oriented frame skips that can spot micro- and macro-expression simultaneously is proposed. This method achieves state-of-the-art performance in SAMM-LV and is competitive in CAS(ME)2, it was used as the baseline result of ME Grand Challenge 2021. The F1-score improves to 0.1036 when trained with composite data consisting of SAMM-LV and SAMMSYNTH. On the unseen ME Grand Challenge 2022 evaluation dataset, it achieves F1-score of 0.1531.
Finally, a new sequence generation method to explore the capability of deep learning network is proposed. It generates spontaneous facial expressions by using only two input sequences without any labels. SSIM and NIQE were used for image quality analysis and the generated data achieved 0.87 and 23.14. By visualising the movements using optical flow value and absolute frame differences, this method demonstrates its potential in generating subtle ME. For realism evaluation, the generated videos were rated by using two facial expression recognition networks
Instant Multi-View Head Capture through Learnable Registration
Existing methods for capturing datasets of 3D heads in dense semantic
correspondence are slow, and commonly address the problem in two separate
steps; multi-view stereo (MVS) reconstruction followed by non-rigid
registration. To simplify this process, we introduce TEMPEH (Towards Estimation
of 3D Meshes from Performances of Expressive Heads) to directly infer 3D heads
in dense correspondence from calibrated multi-view images. Registering datasets
of 3D scans typically requires manual parameter tuning to find the right
balance between accurately fitting the scans surfaces and being robust to
scanning noise and outliers. Instead, we propose to jointly register a 3D head
dataset while training TEMPEH. Specifically, during training we minimize a
geometric loss commonly used for surface registration, effectively leveraging
TEMPEH as a regularizer. Our multi-view head inference builds on a volumetric
feature representation that samples and fuses features from each view using
camera calibration information. To account for partial occlusions and a large
capture volume that enables head movements, we use view- and surface-aware
feature fusion, and a spatial transformer-based head localization module,
respectively. We use raw MVS scans as supervision during training, but, once
trained, TEMPEH directly predicts 3D heads in dense correspondence without
requiring scans. Predicting one head takes about 0.3 seconds with a median
reconstruction error of 0.26 mm, 64% lower than the current state-of-the-art.
This enables the efficient capture of large datasets containing multiple people
and diverse facial motions. Code, model, and data are publicly available at
https://tempeh.is.tue.mpg.de.Comment: Conference on Computer Vision and Pattern Recognition (CVPR) 202
Multi-modal association learning using spike-timing dependent plasticity (STDP)
We propose an associative learning model that can integrate facial images with speech signals to target a subject in a reinforcement learning (RL) paradigm. Through this approach, the rules of learning will involve associating paired stimuli (stimulus–stimulus, i.e., face–speech), which is also known as predictor-choice pairs.
Prior to a learning simulation, we extract the features of the biometrics used in the study. For facial features, we experiment by using two approaches: principal component analysis (PCA)-based Eigenfaces and singular value decomposition (SVD). For speech features, we use wavelet packet decomposition (WPD). The
experiments show that the PCA-based Eigenfaces feature extraction approach produces better results than SVD. We implement the proposed learning model by using the Spike- Timing-Dependent Plasticity (STDP) algorithm, which depends on the time and rate of pre-post synaptic spikes. The key contribution of our study is the implementation of learning rules via STDP and firing rate in spatiotemporal neural networks based on the Izhikevich spiking model. In our learning, we implement learning for response group association by following the reward-modulated STDP in terms of RL, wherein the firing rate of the response groups determines the reward that will be given. We perform a number of experiments that use existing face samples from the Olivetti Research Laboratory (ORL) dataset, and speech samples from TIDigits. After several experiments and simulations are performed to recognize a subject, the results show that the proposed learning model can associate the
predictor (face) with the choice (speech) at optimum performance rates of 77.26% and 82.66% for training and testing, respectively. We also perform learning by using real data, that is, an experiment is conducted on a sample of face–speech data, which have been collected in a manner similar to that of the initial data. The performance results are 79.11% and 77.33% for training and testing, respectively. Based on these results, the proposed learning model can produce high learning performance in terms
of combining heterogeneous data (face–speech). This finding opens possibilities to expand RL in the field of biometric authenticatio
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