- NYCU EveryWhere
- https://boyyeo.github.io/
Stars
Doppelgangers: Learning to Disambiguate Images of Similar Structures
Multiview matching with deep-learning and hand-crafted local features for COLMAP and other SfM software. Supports high-resolution formats and images with rotations. Both CLI and GUI are supported.
iComMa: Inverting 3D Gaussian Splatting for Camera Pose Estimation via Comparing and Matching
TriplaneGaussian: A new hybrid representation for single-view 3D reconstruction.
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.
A PyTorch3D walkthrough and a Medium article 👋 on how to render 3D .obj meshes from various viewpoints to create 2D images.
This repository includes the related code of RaBit.
Official pytorch implementation of the paper: "SinDDM: A Single Image Denoising Diffusion Model"
[CVPR 2024 Highlight] Official PyTorch implementation of CoDeF: Content Deformation Fields for Temporally Consistent Video Processing
Official Pytorch implementation for HNeRV: a hybrid video neural representation (CVPR 2023)
[SIGGRAPH Asia 2023] Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation
A diffuser implementation of Zero123. Zero-1-to-3: Zero-shot One Image to 3D Object (ICCV23)
A unified framework for 3D content generation.
[NeurIPS 2023] Official code of "One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization"
[ICLR 2024 Spotlight] SyncDreamer: Generating Multiview-consistent Images from a Single-view Image
PyTorch implementation of InstructDiffusion, a unifying and generic framework for aligning computer vision tasks with human instructions.
[ICCV 2021] Our work presents a novel neural rendering approach that can efficiently reconstruct geometric and neural radiance fields for view synthesis.
MVDiffusion: Enabling Holistic Multi-view Image Generation with Correspondence-Aware Diffusion, NeurIPS 2023 (spotlight)
A General NeRF Acceleration Toolbox in PyTorch.
Neural Surface reconstruction based on Instant-NGP. Efficient and customizable boilerplate for your research projects. Train NeuS in 10min!
PET-NeuS: Positional Encoding Tri-Planes for Neural Surfaces (CVPR 2023)
[ACM MM23] CLIP-Count: Towards Text-Guided Zero-Shot Object Counting
Official Implementation for "ConceptLab: Creative Generation using Diffusion Prior Constraints"