Ningwei's starred repositories
InstantMesh
InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models
vit-pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
VAED_HeterGraph
The implementation for Interspeech22 "Visually-aware Acoustic Event Detection using Heterogeneous Graphs" paper
Fast-Poisson-Image-Editing
A fast poisson image editing implementation that can utilize multi-core CPU or GPU to handle a high-resolution image input.
fast-poisson-image-editing
Fast, scalable, and extensive implementations of Poisson image editing algorithms.
faster-SadTalker-API
The API server version of the SadTalker project. Runs in Docker, 10 times faster than the original!
VOODOO3D-official
Official implementation for the paper "VOODOO 3D: Volumetric Portrait Disentanglement for One-Shot 3D Head Reenactment"
SadTalker-Video-Lip-Sync
本项目基于SadTalkers实现视频唇形合成的Wav2lip。通过以视频文件方式进行语音驱动生成唇形,设置面部区域可配置的增强方式进行合成唇形(人脸)区域画面增强,提高生成唇形的清晰度。使用DAIN 插帧的DL算法对生成视频进行补帧,补充帧间合成唇形的动作过渡,使合成的唇形更为流畅、真实以及自然。
AnimateAnyone
Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation
GaussianAvatar
[CVPR 2024] The official repo for "GaussianAvatar: Towards Realistic Human Avatar Modeling from a Single Video via Animatable 3D Gaussians"
Gaussian-Head-Avatar
[CVPR 2024] Official repository for "Gaussian Head Avatar: Ultra High-fidelity Head Avatar via Dynamic Gaussians"
gaussian-head
Official repository for 'GaussianHead: High-fidelity Head Avatars with Learnable Gaussian Derivation'
AnimatableGaussians
Code of [CVPR 2024] "Animatable Gaussians: Learning Pose-dependent Gaussian Maps for High-fidelity Human Avatar Modeling"
minddiffusion
A collection of diffusion models based on MindSpore
denoiser
Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)We provide a PyTorch implementation of the paper Real Time Speech Enhancement in the Waveform Domain. In which, we present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities.