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Found 9 papers, 4 papers with code
Learning in a Single Domain for Non-Stationary Multi-Texture Synthesis
To our knowledge, ours is the first scheme for this challenging task, including model, training, and evaluation.
StyleSwap: Style-Based Generator Empowers Robust Face Swapping
Our core idea is to leverage a style-based generator to empower high-fidelity and robust face swapping, thus the generator's advantage can be adopted for optimizing identity similarity.
MobileFaceSwap: A Lightweight Framework for Video Face Swapping
In this work, we propose a lightweight Identity-aware Dynamic Network (IDN) for subject-agnostic face swapping by dynamically adjusting the model parameters according to the identity information.
DFGC 2021: A DeepFake Game Competition
This competition provides a common platform for benchmarking the adversarial game between current state-of-the-art DeepFake creation and detection methods.
FaceController: Controllable Attribute Editing for Face in the Wild
By simply employing some existing and easy-obtainable prior information, our method can control, transfer, and edit diverse attributes of faces in the wild.
Ranked #1 on
Face Swapping
on FaceForensics++
(FID metric)
An Energy Stable C0 Finite Element Scheme for A Phase-Field Model of Vesicle Motion and Deformation
A thermodynamically consistent phase-field model is introduced for simulating motion and shape transformation of vesicles under flow conditions.
Numerical Analysis Numerical Analysis 76T06 92B05 35Q92
Neural Time-Dependent Partial Differential Equation
Inspired by the traditional finite difference and finite elements methods and emerging advancements in machine learning, we propose a sequence-to-sequence learning (Seq2Seq) framework called Neural-PDE, which allows one to automatically learn governing rules of any time-dependent PDE system from existing data by using a bidirectional LSTM encoder, and predict the solutions in next $n$ time steps.
Neural-PDE: A RNN based neural network for solving time dependent PDEs
Partial differential equations (PDEs) play a crucial role in studying a vast number of problems in science and engineering.
Semantically Multi-modal Image Synthesis
Experiments on several challenging datasets demonstrate the superiority of GroupDNet on performing the SMIS task.
