Stars
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Models and examples built with TensorFlow
High-Resolution Image Synthesis with Latent Diffusion Models
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
PyTorch implementations of Generative Adversarial Networks.
A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Torch and Numpy.
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
Synthesizing and manipulating 2048x1024 images with conditional GANs
Efficient 3D human pose estimation in video using 2D keypoint trajectories
Official implementation of CVPR2020 paper "VIBE: Video Inference for Human Body Pose and Shape Estimation"
The author's officially unofficial PyTorch BigGAN implementation.
Learning Chinese Character style with conditional GAN
Pytorch implementation of Self-Attention Generative Adversarial Networks (SAGAN)
Code for reproducing experiments in "Improved Training of Wasserstein GANs"
Pytorch-based tools for visualizing and understanding the neurons of a GAN. https://gandissect.csail.mit.edu/
A pytorch implementation of Paper "Improved Training of Wasserstein GANs"
Deep learning with cats (^._.^)
Official code of "HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation", CVPR 2021
An implementation of CycleGan using TensorFlow
A Tensorflow implementation of Spatial Transformer Networks.
🔥🔥 PyTorch implementation of "Progressive growing of GANs (PGGAN)" 🔥🔥
Tensorflow implementation for learning an image-to-image translation without input-output pairs. https://arxiv.org/pdf/1703.10593.pdf
[ICCV 2021, Oral] PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop
Unofficial PyTorch implementation of the paper titled "Progressive growing of GANs for improved Quality, Stability, and Variation"
The project is an official implementation of our paper "3D Human Pose Estimation with Spatial and Temporal Transformers".
Implementations of (theoretical) generative adversarial networks and comparison without cherry-picking