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[ICLR 2023 Spotlight] EVA3D: Compositional 3D Human Generation from 2D Image Collections
📺 An End-to-End Solution for High-Resolution and Long Video Generation Based on Transformer Diffusion
DECA: Detailed Expression Capture and Animation (SIGGRAPH 2021)
3DGS-Avatar: Animatable Avatars via Deformable 3D Gaussian Splatting
[CVPR2024] Official implementation of SplattingAvatar.
Expressive Gaussian Human Avatars from Monocular RGB Video (NeurIPS 2024)
This repository contains code corresponding to the paper Video based reconstruction of 3D people models.
[CVPR 2024] Official PyTorch implementation of SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering
[CVPR 2023] Official implementation of the paper "One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer"
[CVPR'23, Highlight] ECON: Explicit Clothed humans Optimized via Normal integration
DressRecon: Freeform 4D Human Reconstruction from Monocular Video
[CVPR 2024] MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model
Example code for the FLAME 3D head model. The code demonstrates how to sample 3D heads from the model, fit the model to 3D keypoints and 3D scans.
Official PyTorch implementation of "Expressive Whole-Body 3D Gaussian Avatar", ECCV 2024.
Repository for ICCV23 paper: "ReFit: Recurrent Fitting Network for 3D Human Recovery"
Official Implementation of paper "Disentangled Clothed Avatar Generation from Text Descriptions"
4DHumans: Reconstructing and Tracking Humans with Transformers
OpenMMLab Pose Estimation Toolbox and Benchmark.
This repository collects awesome survey, resource, and paper for lifelong learning LLM agents
The official implementation of the paper "Human Motion Diffusion as a Generative Prior"
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.
Official implementation for "Generating Diverse and Natural 3D Human Motions from Texts (CVPR2022)."
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.