Paper list: deep learning based video compression

CCEdit: Creative and Controllable Video Editing via Diffusion Models

Low-complexity neural image & video codec.

Entropy coders for research and production in Python and Rust.

A beautiful, simple, clean, and responsive Jekyll theme for academics

Original reference implementation of "3D Gaussian Splatting for Real-Time Radiance Field Rendering"

A collection of tools for neural compression enthusiasts.

LICoris: Streaming-capable High-performance Architecture of Learned Image Compression Codecs

Recoil: Parallel rANS Decoding with Decoder-Adaptive Scalability

Spatial Sparse Convolution Library

Productive, portable, and performant GPU programming in Python.

Open3D: A Modern Library for 3D Data Processing

A Code Release for Mip-NeRF 360, Ref-NeRF, and RawNeRF

Enhance and restore any things continuously with Segment Anything

A curated list of awesome neural radiance fields papers

AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.

Let us control diffusion models!

Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything

Inpaint anything using Segment Anything and inpainting models.

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

Implement minimal boilerplate CLIs derived from type hints and parse from command line, config files and environment variables

Pretrain, finetune ANY AI model of ANY size on multiple GPUs, TPUs with zero code changes.

Seamless operability between C 11 and Python

A PyTorch library and evaluation platform for end-to-end compression research

Taming Transformers for High-Resolution Image Synthesis

CVPR18: Learning and Using the Arrow of Time