Popular GitHub Repositories for Css Animation

Enhancing code comprehension with AI-driven explanations and a secure code snippet repository.

This repository contains the source code for the paper First Order Motion Model for Image Animation

[CVPR 2023] SadTalker：Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation

? Yao is an all-in-one application engine that enables developers to create web apps, REST APIs, business applications, and more, with AI as a development partner.

[CVPR 2022] Thin-Plate Spline Motion Model for Image Animation.

Official pytorch implementation of the paper: "SinGAN: Learning a Generative Model from a Single Natural Image"

A list of StableDiffusion styles and some notes for offline use. Pure HTML, CSS and a bit of JS.

GANimation: Anatomically-aware Facial Animation from a Single Image (ECCV'18 Oral) [PyTorch]

Use AnimeGANv3 to make your own animation works, including turning photos or videos into anime.

An end-to-end library for editing and rendering motion of 3D characters with deep learning [SIGGRAPH 2020]

[CVPR'22] ICON: Implicit Clothed humans Obtained from Normals

[NeurIPS 2020] Official code for the paper "DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation". Includes a PyTorch library for deep learning with SVG data.

Wunjo CE: Face Swap, Lip Sync, Control Remove Objects & Text & Background, Restyling, Audio Separator, Clone Voice, Video Generation. Open Source, Local & Free.

Easily generate thousands of 3D models, images, and animation automatically in Blender for free with Blend_My_NFTs.

Official code for CVPR2022 paper: Depth-Aware Generative Adversarial Network for Talking Head Video Generation

A modern chat interface for AI agents built with Next.js, Tailwind CSS, and TypeScript.

Here we will keep track of the latest AI Game Development Tools, including LLM, Agent, Code, Writer, Image, Texture, Shader, 3D Model, Animation, Video, Audio, Music, Singing Voice and Analytics. ?

We present MocapNET, a real-time method that estimates the 3D human pose directly in the popular Bio Vision Hierarchy (BVH) format, given estimations of the 2D body joints originating from monocular color images. Our contributions include: (a) A novel and compact 2D pose NSRM representation. (b) A human body orientation classifier and an ensemble of orientation-tuned neural networks that regress the 3D human pose by also allowing for the decomposition of the body to an upper and lower kinematic hierarchy. This permits the recovery of the human pose even in the case of significant occlusions. (c) An efficient Inverse Kinematics solver that refines the neural-network-based solution providing 3D human pose estimations that are consistent with the limb sizes of a target person (if known). All the above yield a 33% accuracy improvement on the

[CVPR 2022] FaceFormer: Speech-Driven 3D Facial Animation with Transformers

Diffusion-based Portrait and Animal Animation