Object Database for Super Mario Galaxy 1/2.

Overview

Super Mario Galaxy Object Database

Welcome to the public object database for Super Mario Galaxy and Super Mario Galaxy 2. Here, we document all objects and classes that can be found in the Galaxy games. This includes information about their setup, properties and usage in the game. Everybody can contribute to this project. Please make sure that you've joined the Luma's Workshop Discord server. That's where major Galaxy modding and documentation takes place. Here's a short overview of all features:

  • Contains information about all objects and their classes.
  • Viewable dumps of all object occurrences in any stage.
  • Generator for Whitehole's (outdated) Object Database format.

All information about objects and classes are stored in the respective JSON files to keep things organized. For editing, please use the editor instead. It's easier and takes care of potential mistakes. XML files for use with Whitehole can be easily generated as well!

Setup

If you want to contribute, you have to set up some things. You can find plenty of tutorials regarding the setup of these if you are unsure:

  • Python 3.9 or newer. This specific version is needed for the Whitehole XML generator.
  • PyQt5, the Qt binding for Python. Install it using pip install PyQt5.
  • qdarkstyle, the dark mode interface. Install it using pip install qdarkstyle.

Guideline

  • As you can see, information is split between objects and classes. The main information about setups, functionality and parameters belong to the class specifications. Additional information, like a proper name for an object and brief descriptions belong to the object information.
  • As of now, we document the objects from Super Mario Galaxy 2 only. Some objects and classes differ from their SMG1 counterparts. It will be hard to keep track of these differences if we mix in the research for both games at once. Therefore, we'll have to finish the SMG2 stuff first. But SMG1's objects and classes will definitely be added in the future.
  • Don't mark a class as finished/complete! I still need to verify if the information is correct by looking into the game's code.
  • There are some class parameters that are only usable by specific objects, for example SunakazeKun's Obj_arg0. You can list any exclusive objects in a parameters "Exclusive" list.
  • If you want to specify special values for a parameter, you can do that using the "Values" field. Each line corresponds to a different value.
  • Game specific terms should be treated like names. Starbit or starbit becomes Star Bit, coins becomes Coins, ground pound becomes Ground Pound and so on.
  • Most of the time, categories are pretty straightforward. However, you may get confused about Stage Parts and Level Features. The former includes objects that you can find in specific galaxies. The latter includes stuff like the crystal cages, various decorative objects and reusable assets that may not really be specific to a stage. If you are unsure, just ask me.
  • Keep the usage of rounded brackets at a minimum. Put this in square brackets instead. Also, keep naming objects like "Version A" or "Section B" at a minimum. Try to be precise.
  • For Stage Parts, make sure to include the name of the stage in the object's descriptive name. Examples: "Rightside Down -- Intro Planet", "Rolling Coaster -- Star Ball Opener", "Battle Belt -- Land Urchin Planet", ...
Owner
Aurum
German video game modder. Currently doing my bachelor.
Aurum
The code for our CVPR paper PISE: Person Image Synthesis and Editing with Decoupled GAN, Project Page, supp.

PISE The code for our CVPR paper PISE: Person Image Synthesis and Editing with Decoupled GAN, Project Page, supp. Requirement conda create -n pise pyt

jinszhang 110 Nov 21, 2022
Jarvis Project is a basic virtual assistant that uses TensorFlow for learning.

Jarvis_proyect Jarvis Project is a basic virtual assistant that uses TensorFlow for learning. Latest version 0.1 Features: Good morning protocol Tell

Anze Kovac 3 Aug 31, 2022
NAS-Bench-x11 and the Power of Learning Curves

NAS-Bench-x11 NAS-Bench-x11 and the Power of Learning Curves Shen Yan, Colin White, Yash Savani, Frank Hutter. NeurIPS 2021. Surrogate NAS benchmarks

AutoML-Freiburg-Hannover 13 Nov 18, 2022
BiSeNet based on pytorch

BiSeNet BiSeNet based on pytorch 0.4.1 and python 3.6 Dataset Download CamVid dataset from Google Drive or Baidu Yun(6xw4). Pretrained model Download

367 Dec 26, 2022
Vision Transformer for 3D medical image registration (Pytorch).

ViT-V-Net: Vision Transformer for Volumetric Medical Image Registration keywords: vision transformer, convolutional neural networks, image registratio

Junyu Chen 192 Dec 20, 2022
Plato: A New Framework for Federated Learning Research

a new software framework to facilitate scalable federated learning research.

System <a href=[email protected] Lab"> 192 Jan 05, 2023
[RSS 2021] An End-to-End Differentiable Framework for Contact-Aware Robot Design

DiffHand This repository contains the implementation for the paper An End-to-End Differentiable Framework for Contact-Aware Robot Design (RSS 2021). I

Jie Xu 60 Jan 04, 2023
Evaluation toolkit of the informative tracking benchmark comprising 9 scenarios, 180 diverse videos, and new challenges.

Informative-tracking-benchmark Informative tracking benchmark (ITB) higher diversity. It contains 9 representative scenarios and 180 diverse videos. m

Xin Li 15 Nov 26, 2022
Code for the Paper: Alexandra Lindt and Emiel Hoogeboom.

Discrete Denoising Flows This repository contains the code for the experiments presented in the paper Discrete Denoising Flows [1]. To give a short ov

Alexandra Lindt 3 Oct 09, 2022
Automatic meme generation model using Tensorflow Keras.

Memefly You can find the project at MemeflyAI. Contributors Nick Buukhalter Harsh Desai Han Lee Project Overview Trello Board Product Canvas Automatic

BloomTech Labs 2 Jan 13, 2022
Implementation of Bidirectional Recurrent Independent Mechanisms (Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules)

BRIMs Bidirectional Recurrent Independent Mechanisms Implementation of the paper Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neura

Sarthak Mittal 26 May 26, 2022
Aiming at the common training datsets split, spectrum preprocessing, wavelength select and calibration models algorithm involved in the spectral analysis process

Aiming at the common training datsets split, spectrum preprocessing, wavelength select and calibration models algorithm involved in the spectral analysis process, a complete algorithm library is esta

Fu Pengyou 50 Jan 07, 2023
PyTorch implementation for "Sharpness-aware Quantization for Deep Neural Networks".

Sharpness-aware Quantization for Deep Neural Networks Recent Update 2021.11.23: We release the source code of SAQ. Setup the environments Clone the re

Zhuang AI Group 30 Dec 19, 2022
CTRL-C: Camera calibration TRansformer with Line-Classification

CTRL-C: Camera calibration TRansformer with Line-Classification This repository contains the official code and pretrained models for CTRL-C (Camera ca

57 Nov 14, 2022
Locally cache assets that are normally streamed in POPULATION: ONE

Population One Localizer This is no longer needed as of the build shipped on 03/03/22, thank you bigbox :) Locally cache assets that are normally stre

Ahman Woods 2 Mar 04, 2022
A PyTorch implementation of "SimGNN: A Neural Network Approach to Fast Graph Similarity Computation" (WSDM 2019).

SimGNN ⠀⠀⠀ A PyTorch implementation of SimGNN: A Neural Network Approach to Fast Graph Similarity Computation (WSDM 2019). Abstract Graph similarity s

Benedek Rozemberczki 534 Dec 25, 2022
Official implementation of the paper "Lightweight Deep CNN for Natural Image Matting via Similarity Preserving Knowledge Distillation"

Lightweight-Deep-CNN-for-Natural-Image-Matting-via-Similarity-Preserving-Knowledge-Distillation Introduction Accepted at IEEE Signal Processing Letter

DongGeun-Yoon 19 Jun 07, 2022
TRIQ implementation

TRIQ Implementation TF-Keras implementation of TRIQ as described in Transformer for Image Quality Assessment. Installation Clone this repository. Inst

Junyong You 115 Dec 30, 2022
[NeurIPS 2020] Code for the paper "Balanced Meta-Softmax for Long-Tailed Visual Recognition"

Balanced Meta-Softmax Code for the paper Balanced Meta-Softmax for Long-Tailed Visual Recognition Jiawei Ren, Cunjun Yu, Shunan Sheng, Xiao Ma, Haiyu

Jiawei Ren 65 Dec 21, 2022
A community run, 5-day PyTorch Deep Learning Bootcamp

Deep Learning Winter School, November 2107. Tel Aviv Deep Learning Bootcamp : http://deep-ml.com. About Tel-Aviv Deep Learning Bootcamp is an intensiv

Shlomo Kashani. 1.3k Sep 04, 2021