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  • Generator for Whitehole's (outdated) Object Database format.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
Supplemental Code for "ImpressionNet :A Multi view Approach to Predict Socio Facial Impressions"

Supplemental Code for "ImpressionNet :A Multi view Approach to Predict Socio Facial Impressions" Environment requirement This code is based on Python

Rohan Kumar Gupta 1 Dec 19, 2021
Human annotated noisy labels for CIFAR-10 and CIFAR-100.

Dataloader for CIFAR-N CIFAR-10N noise_label = torch.load('./data/CIFAR-10_human.pt') clean_label = noise_label['clean_label'] worst_label = noise_lab

<a href=[email protected]"> 117 Nov 30, 2022
QR2Pass-project - A proof of concept for an alternative (passwordless) authentication system to a web server

QR2Pass This is a proof of concept for an alternative (passwordless) authenticat

4 Dec 09, 2022
Improving Calibration for Long-Tailed Recognition (CVPR2021)

MiSLAS Improving Calibration for Long-Tailed Recognition Authors: Zhisheng Zhong, Jiequan Cui, Shu Liu, Jiaya Jia [arXiv] [slide] [BibTeX] Introductio

Jia Research Lab 116 Dec 20, 2022
Paper Title: Heterogeneous Knowledge Distillation for Simultaneous Infrared-Visible Image Fusion and Super-Resolution

HKDnet Paper Title: "Heterogeneous Knowledge Distillation for Simultaneous Infrared-Visible Image Fusion and Super-Resolution" Email:

wasteland 11 Nov 12, 2022
Code accompanying our NeurIPS 2021 traffic4cast challenge

Traffic forecasting on traffic movie snippets This repo contains all code to reproduce our approach to the IARAI Traffic4cast 2021 challenge. In the c

Nina Wiedemann 2 Aug 09, 2022
SlideGraph+: Whole Slide Image Level Graphs to Predict HER2 Status in Breast Cancer

SlideGraph+: Whole Slide Image Level Graphs to Predict HER2 Status in Breast Cancer A novel graph neural network (GNN) based model (termed SlideGraph+

28 Dec 24, 2022
PaSST: Efficient Training of Audio Transformers with Patchout

PaSST: Efficient Training of Audio Transformers with Patchout This is the implementation for Efficient Training of Audio Transformers with Patchout Pa

165 Dec 26, 2022
PyTorch Implementation of CycleGAN and SSGAN for Domain Transfer (Minimal)

MNIST-to-SVHN and SVHN-to-MNIST PyTorch Implementation of CycleGAN and Semi-Supervised GAN for Domain Transfer. Prerequites Python 3.5 PyTorch 0.1.12

Yunjey Choi 401 Dec 30, 2022
Explaining neural decisions contrastively to alternative decisions.

Contrastive Explanations for Model Interpretability This is the repository for the paper "Contrastive Explanations for Model Interpretability", about

AI2 16 Oct 16, 2022
Image Segmentation Animation using Quadtree concepts.

QuadTree Image Segmentation Animation using QuadTree concepts. Usage usage: quad.py [-h] [-fps FPS] [-i ITERATIONS] [-ws WRITESTART] [-b] [-img] [-s S

Alex Eidt 29 Dec 25, 2022
Official repository for the paper "Going Beyond Linear Transformers with Recurrent Fast Weight Programmers"

Recurrent Fast Weight Programmers This is the official repository containing the code we used to produce the experimental results reported in the pape

IDSIA 36 Nov 15, 2022
LSTC: Boosting Atomic Action Detection with Long-Short-Term Context

LSTC: Boosting Atomic Action Detection with Long-Short-Term Context This Repository contains the code on AVA of our ACM MM 2021 paper: LSTC: Boosting

Tencent YouTu Research 9 Oct 11, 2022
This repository contains the files for running the Patchify GUI.

Repository Name Train-Test-Validation-Dataset-Generation App Name Patchify Description This app is designed for crop images and creating smal

Salar Ghaffarian 9 Feb 15, 2022
Quantized models with python

quantized-network download .pth files to qmodels/: googlenet : https://download.

adreamxcj 2 Dec 28, 2021
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity

[ICLR 2022] Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity by Shiwei Liu, Tianlong Chen, Zahra Atashgahi, Xiaohan Chen, Ghada Sokar, Elen

VITA 18 Dec 31, 2022
Fast Neural Representations for Direct Volume Rendering

Fast Neural Representations for Direct Volume Rendering Sebastian Weiss, Philipp Hermüller, Rüdiger Westermann This repository contains the code and s

Sebastian Weiss 20 Dec 03, 2022
Code for paper "Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation" EMNLP 2021

The repo provides the code for paper "Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation" EMNLP 2

Yuning Mao 18 May 24, 2022
Implementations for the ICLR-2021 paper: SEED: Self-supervised Distillation For Visual Representation.

Implementations for the ICLR-2021 paper: SEED: Self-supervised Distillation For Visual Representation.

Jacob 27 Oct 23, 2022
PPO is a very popular Reinforcement Learning algorithm at present.

PPO is a very popular Reinforcement Learning algorithm at present. OpenAI takes PPO as the current baseline algorithm. We use the PPO algorithm to train a policy to give the best action in any situat

Rosefintech 11 Aug 23, 2021