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  • Generator for Whitehole's (outdated) Object Database format.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
Repository for Multimodal AutoML Benchmark

Benchmarking Multimodal AutoML for Tabular Data with Text Fields Repository for the NeurIPS 2021 Dataset Track Submission "Benchmarking Multimodal Aut

Xingjian Shi 44 Nov 24, 2022
Nicholas Lee 3 Jan 09, 2022
Keras Model Implementation Walkthrough

Keras Model Implementation Walkthrough

Luke Wood 17 Sep 27, 2022
ICCV2021, Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet

Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet, ICCV 2021 Update: 2021/03/11: update our new results. Now our T2T-ViT-14 w

YITUTech 1k Dec 31, 2022
"Neural Turing Machine" in Tensorflow

Neural Turing Machine in Tensorflow Tensorflow implementation of Neural Turing Machine. This implementation uses an LSTM controller. NTM models with m

Taehoon Kim 1k Dec 06, 2022
Supercharging Imbalanced Data Learning WithCausal Representation Transfer

ECRT: Energy-based Causal Representation Transfer Code for Supercharging Imbalanced Data Learning With Energy-basedContrastive Representation Transfer

Zidi Xiu 11 May 02, 2022
[CVPR 2022 Oral] MixFormer: End-to-End Tracking with Iterative Mixed Attention

MixFormer The official implementation of the CVPR 2022 paper MixFormer: End-to-End Tracking with Iterative Mixed Attention [Models and Raw results] (G

Multimedia Computing Group, Nanjing University 235 Jan 03, 2023
Unofficial implementation of Pix2SEQ

Unofficial-Pix2seq: A Language Modeling Framework for Object Detection Unofficial implementation of Pix2SEQ. Please use this code with causion. Many i

159 Dec 12, 2022
PyTorch Implementation of our paper Explain Me the Painting: Multi-Topic Knowledgeable Art Description Generation

PyTorch Implementation of our paper Explain Me the Painting: Multi-Topic Knowledgeable Art Description Generation

Zechen Bai 12 Jul 08, 2022
The 2nd place solution of 2021 google landmark retrieval on kaggle.

Leaderboard, taxonomy, and curated list of few-shot object detection papers.

229 Dec 13, 2022
Real life contra a deep learning project built using mediapipe and openc

real-life-contra Description A python script that translates the body movement into in game control. Welcome to all new real life contra a deep learni

Programminghut 7 Jan 26, 2022
VOGUE: Try-On by StyleGAN Interpolation Optimization

VOGUE is a StyleGAN interpolation optimization algorithm for photo-realistic try-on. Top: shirt try-on automatically synthesized by our method in two different examples.

Wei ZHANG 66 Dec 09, 2022
Spherical CNNs

Spherical CNNs Equivariant CNNs for the sphere and SO(3) implemented in PyTorch Overview This library contains a PyTorch implementation of the rotatio

Jonas Köhler 893 Dec 28, 2022
CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes (AAAI2022)

CMUA-Watermark The official code for CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes (AAAI2022) arxiv. It is bas

50 Nov 26, 2022
Toolchain to build Yoshi's Island from source code

Project-Y Toolchain to build Yoshi's Island (J) V1.0 from source code, by MrL314 Last updated: September 17, 2021 Setup To begin, download this toolch

MrL314 19 Apr 18, 2022
Minimal implementation of PAWS (https://arxiv.org/abs/2104.13963) in TensorFlow.

PAWS-TF 🐾 Implementation of Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples (PAWS)

Sayak Paul 43 Jan 08, 2023
Tackling data scarcity in Speech Translation using zero-shot multilingual Machine Translation techniques

Tackling data scarcity in Speech Translation using zero-shot multilingual Machine Translation techniques This repository is derived from the NMTGMinor

Tu Anh Dinh 1 Sep 07, 2022
Paddle Graph Learning (PGL) is an efficient and flexible graph learning framework based on PaddlePaddle

DOC | Quick Start | 中文 Breaking News !! 🔥 🔥 🔥 OGB-LSC KDD CUP 2021 winners announced!! (2021.06.17) Super excited to announce our PGL team won TWO

1.5k Jan 06, 2023
SHIFT15M: multiobjective large-scale fashion dataset with distributional shifts

[arXiv] The main motivation of the SHIFT15M project is to provide a dataset that contains natural dataset shifts collected from a web service IQON, wh

ZOZO, Inc. 138 Nov 24, 2022
Fre-GAN: Adversarial Frequency-consistent Audio Synthesis

Fre-GAN Vocoder Fre-GAN: Adversarial Frequency-consistent Audio Synthesis Training: python train.py --config config.json Citation: @misc{kim2021frega

Rishikesh (ऋषिकेश) 93 Dec 17, 2022