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Implementation of the SIGGRAPH 2024 conference paper "High-quality Surface Reconstruction using Gaussian Surfels".
🎞️ [NeurIPS'24] MVSplat360: Feed-Forward 360 Scene Synthesis from Sparse Views
[SIGGRAPH'24] 2D Gaussian Splatting for Geometrically Accurate Radiance Fields
Curated list of papers and resources focused on 3D Gaussian Splatting, intended to keep pace with the anticipated surge of research in the coming months.
Stable diffusion for real-time music generation
Audio Dataset for training CLAP and other models
Audiocraft is a library for audio processing and generation with deep learning. It features the state-of-the-art EnCodec audio compressor / tokenizer, along with MusicGen, a simple and controllable…
Generative models for conditional audio generation
3D Gaussian Splatting Converter: A tool to seamlessly convert 3DGS .ply files to a Cloud Compare-friendly format and vice-versa. Features include RGB coloring, density filtering, and flyer removal …
[CVPR 2024] GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splatting
3DGS-to-PC: Convert a 3D gaussian splatting scene into a dense point cloud or basic mesh with advanced customisation options and high-accuracy rendered point colours
YouTube Music Desktop App bundled with custom plugins (and built-in ad blocker / downloader)
Ongoing research training gaussian splatting at scale by distributed system
A Unified Framework for Surface Reconstruction
[CVPR 2024] Official PyTorch implementation of SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering
DN-Splatter + AGS-Mesh: Depth and Normal Priors for Gaussian Splatting
An extremely fast Python package and project manager, written in Rust.
✂️ Automated high-quality background removal framework for an image using neural networks. ✂️
A collection of audio autoencoders, in PyTorch.
Metrics for evaluating music and audio generative models – with a focus on long-form, full-band, and stereo generations.
The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use th…
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