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Found 34 papers, 13 papers with code
Robust 3D Human Motion Reconstruction Via Dynamic Template Construction
In multi-view human body capture systems, the recovered 3D geometry or even the acquired imagery data can be heavily corrupted due to occlusions, noise, limited field of- view, etc.
4D Human Body Correspondences from Panoramic Depth Maps
The availability of affordable 3D full body reconstruction systems has given rise to free-viewpoint video (FVV) of human shapes.
PIV-Based 3D Fluid Flow Reconstruction Using Light Field Camera
Particle Imaging Velocimetry (PIV) estimates the flow of fluid by analyzing the motion of injected particles.
Talking-head Generation with Rhythmic Head Motion
When people deliver a speech, they naturally move heads, and this rhythmic head motion conveys prosodic information.
Complexity Measures for Neural Networks with General Activation Functions Using Path-based Norms
The approach is motivated by approximating the general activation functions with one-dimensional ReLU networks, which reduces the problem to the complexity controls of ReLU networks.
On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis
We study the approximation properties and optimization dynamics of recurrent neural networks (RNNs) when applied to learn input-output relationships in temporal data.
PoP-Net: Pose over Parts Network for Multi-Person 3D Pose Estimation from a Depth Image
In this paper, a real-time method called PoP-Net is proposed to predict multi-person 3D poses from a depth image.
RobustFusion: Robust Volumetric Performance Reconstruction under Human-object Interactions from Monocular RGBD Stream
To fill this gap, in this paper, we propose RobustFusion, a robust volumetric performance reconstruction system for human-object interaction scenarios using only a single RGBD sensor, which combines various data-driven visual and interaction cues to handle the complex interaction patterns and severe occlusions.
NeuLF: Efficient Novel View Synthesis with Neural 4D Light Field
In this paper, we present an efficient and robust deep learning solution for novel view synthesis of complex scenes.
Approximation Theory of Convolutional Architectures for Time Series Modelling
We study the approximation properties of convolutional architectures applied to time series modelling, which can be formulated mathematically as a functional approximation problem.
On the approximation properties of recurrent encoder-decoder architectures
Our results provide the theoretical understanding of approximation properties of the recurrent encoder-decoder architecture, which characterises, in the considered setting, the types of temporal relationships that can be efficiently learned.
Incremental Unsupervised Feature Selection for Dynamic Incomplete Multi-view Data
To address these issues, we propose an Incremental Incomplete Multi-view Unsupervised Feature Selection method (I$^2$MUFS) on incomplete multi-view streaming data.
Feature Selection for Fault Detection and Prediction based on Event Log Analysis
Event logs are widely used for anomaly detection and prediction in complex systems.
A Survey on Explainable Anomaly Detection
In the past two decades, most research on anomaly detection has focused on improving the accuracy of the detection, while largely ignoring the explainability of the corresponding methods and thus leaving the explanation of outcomes to practitioners.
NeRFPlayer: A Streamable Dynamic Scene Representation with Decomposed Neural Radiance Fields
Visually exploring in a real-world 4D spatiotemporal space freely in VR has been a long-term quest.
3D-Aware Facial Landmark Detection via Multi-View Consistent Training on Synthetic Data
Accurate facial landmark detection on wild images plays an essential role in human-computer interaction, entertainment, and medical applications.
Robust 3D Shape Classification via Non-Local Graph Attention Network
Especially, in the case of sparse point clouds (64 points) with noise under arbitrary SO(3) rotation, the classification result (85. 4%) of NLGAT is improved by 39. 4% compared with the best development of other methods.
MAC: A unified framework boosting low resource automatic speech recognition
Furthermore, in the ASR task, MAC beats wav2vec2 (with fine-tuning) on common voice datasets of Cantonese and gets really competitive results on common voice datasets of Taiwanese and Japanese.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Explainable Contextual Anomaly Detection using Quantile Regression Forests
Traditional anomaly detection methods aim to identify objects that deviate from most other objects by treating all features equally.
A Brief Survey on the Approximation Theory for Sequence Modelling
We survey current developments in the approximation theory of sequence modelling in machine learning.
Harnessing Low-Frequency Neural Fields for Few-Shot View Synthesis
We further propose a simple-yet-effective strategy for tuning the frequency to avoid overfitting few-shot inputs: enforcing consistency among the frequency domain of rendered 2D images.
Inverse Approximation Theory for Nonlinear Recurrent Neural Networks
We prove an inverse approximation theorem for the approximation of nonlinear sequence-to-sequence relationships using recurrent neural networks (RNNs).
Graph Neural Networks based Log Anomaly Detection and Explanation
Event logs are widely used to record the status of high-tech systems, making log anomaly detection important for monitoring those systems.
High Fidelity 3D Hand Shape Reconstruction via Scalable Graph Frequency Decomposition
To capture high-frequency personalized details, we transform the 3D mesh into the frequency domain, and propose a novel frequency decomposition loss to supervise each frequency component.
Uncertainty-aware State Space Transformer for Egocentric 3D Hand Trajectory Forecasting
In this paper, we set up an egocentric 3D hand trajectory forecasting task that aims to predict hand trajectories in a 3D space from early observed RGB videos in a first-person view.
OpenIllumination: A Multi-Illumination Dataset for Inverse Rendering Evaluation on Real Objects
We introduce OpenIllumination, a real-world dataset containing over 108K images of 64 objects with diverse materials, captured under 72 camera views and a large number of different illuminations.
NeuRBF: A Neural Fields Representation with Adaptive Radial Basis Functions
The spatial positions of their neural features are fixed on grid nodes and cannot well adapt to target signals.
Relit-NeuLF: Efficient Relighting and Novel View Synthesis via Neural 4D Light Field
A DecomposeNet learns to map each ray to its SVBRDF components: albedo, normal, and roughness.
On the Generalization Properties of Diffusion Models
This precisely elucidates the adverse effect of "modes shift" in ground truths on the model generalization.
Spacetime Gaussian Feature Splatting for Real-Time Dynamic View Synthesis
Novel view synthesis of dynamic scenes has been an intriguing yet challenging problem.
TechGPT-2.0: A large language model project to solve the task of knowledge graph construction
Large language models have exhibited robust performance across diverse natural language processing tasks.
Spectrum AUC Difference (SAUCD): Human-aligned 3D Shape Evaluation
Then, we calculate the Area Under the Curve (AUC) difference between the two spectrums, so that each frequency band that captures either the overall or detailed shape is equitably considered.
Monocular 3D Object Detection via Feature Domain Adaptation
Monocular 3D object detection is a challenging task due to unreliable depth, resulting in a distinct performance gap between monocular and LiDAR-based approaches.
3D Fluid Flow Reconstruction Using Compact Light Field PIV
In this paper, we present a PIV solution that uses a compact lenslet-based light field camera to track dense particles floating in the fluid and reconstruct the 3D fluid flow.
