Grow Function: Generate 3D Stacked Bifurcating Double Deep Cellular Automata based organisms which differentiate using a Genetic Algorithm...

Related tags

Deep LearningGrowF
Overview

GrowF

Banner

Grow Function: Generate 3D Stacked Bifurcating Double Deep Cellular Automata based organisms which differentiate using a Genetic Algorithm...

TLDR; High Def Living Trees that you can breed, trim and mint as NFTs on Solana, Ethereum, Cardano and other blockchain networks.

This demo represents the current state of the codebase. If anyone wishes to join this project, please contact or fork.

Written in Python using the Blender Library

Current state of development https://www.youtube.com/watch?v=BUarQzuhj1c

Installation

  • Install Blender if you don't already have it
  • Open treegen.blend

Once opened, you can generate whatever tree is currently there by default by going to the scripting tab, opening tree.py and pressing the Run button

The Metaverse Needs Trees

Let's Face it, it's hard to model trees that are realistic in any 3D modeling platform. Much less, make them lowpoly or high poly, or ready for video games or kinematics and physics. But if you think about it, it's hard to make believable trees because trees are grown, not sculpted by nature. They the results of a bunch of growth patterns that came together to form what we categorize as trees or even any type of plant. The bifurcation structure of a tree is everywhere. You needen't go as far as looking at the human nervous system, or in the structure of folders in your computer, to see that same tree organization. Imagine having a tree that you can plant on top of a structure like a stone sculpture in VR and watch it's roots eating through the walls and stone in hyper-real time. Imagine breeding trees to be a specific color or give a specific type of fruit, or growing them in zero gravity. Imagine planting a garden and watching your plants and trees grow together over time and generations.

We Have the Technology

Recent developments in the fields of Cellular Automata and Genetic Algorithms have led to the possibility of growing living organisms in higher dimensions. Many projects like Lenia, The Life Engine and even VR games like Playne, and Inward have made a big deal of living organisms in games and tech culture. These living organisms can behave like bacteria, like larger soft-bodied oganisms, or like Trees. On a scientific level, there exist virtual living ecosystems of over 500,000 plants in simulations like the ones seen in the paper "Synthetic Silviculture: Multi-Scale Modeling of Plant Ecosystems". These large or multi-scale simulations are focused on larger scale simulations, creating realistic, yet estimated details, (albeit through rigorous scientific analysis to approximate reality).

But that begs the question, how high definition can one go in creating growing 3D systems? Can cell differentiation be accquired by genetic algorithm in new and spontaneous ways which account for ecosystem? This project aims to advance toward answering those questions, starting with what we think will end up being hi-res tree models, but could end up as anything.

I think it's possible to go to infinite resolution by making a growth protocol that is forward compatible, so that the computers of the future which operate on orders of magnitude of higher parallelism can show the same tree you minted in 2022, in much higher detail.

The Blockchain? Why?

Well, trees, and other living virtual organisms can be owned. The 3D Models of their life progression can be included in games using Non-Fungible Tokens (NFTs). If you can own any unique digital item, why not own a tree that you have bred, or trimmed, or simply that you found in a VR game somewhere. Think about trimming, or when trees bear fruits that have specific properties or visual peculiarities. Each branch of the tree can be removed, the fruits or leaves, (or whatever ends up growing on a bifurcation) can be separated from the tree, recorded as removed in the tree's history (affecting it's model forever). Just like with real trees, the seeds in those fruits can be minted and given to friends or sold, reflecting the value that you have added to the tree by planting and growing it somewhere in the Metaverse. It could end up seeding virtual forests, or being used as CG set pieces in movies or video games depending on who buys it from you.

Owner
Nathaniel Gibson
Nathaniel Gibson
The official code for paper "R2D2: Recursive Transformer based on Differentiable Tree for Interpretable Hierarchical Language Modeling".

R2D2 This is the official code for paper titled "R2D2: Recursive Transformer based on Differentiable Tree for Interpretable Hierarchical Language Mode

Alipay 49 Dec 17, 2022
Shallow Convolutional Neural Networks for Human Activity Recognition using Wearable Sensors

-IEEE-TIM-2021-1-Shallow-CNN-for-HAR [IEEE TIM 2021-1] Shallow Convolutional Neural Networks for Human Activity Recognition using Wearable Sensors All

Wenbo Huang 1 May 17, 2022
[NeurIPS 2021] A weak-shot object detection approach by transferring semantic similarity and mask prior.

[NeurIPS 2021] A weak-shot object detection approach by transferring semantic similarity and mask prior.

BCMI 49 Jul 27, 2022
ROS-UGV-Control-Interface - Control interface which can be used in any UGV

ROS-UGV-Control-Interface Cam Closed: Cam Opened:

Ahmet Fatih Akcan 1 Nov 04, 2022
Implementation of ToeplitzLDA for spatiotemporal stationary time series data.

Code for the ToeplitzLDA classifier proposed in here. The classifier conforms sklearn and can be used as a drop-in replacement for other LDA classifiers. For in-depth usage refer to the learning from

Jan Sosulski 5 Nov 07, 2022
Surrogate-Assisted Genetic Algorithm for Wrapper Feature Selection

SAGA Surrogate-Assisted Genetic Algorithm for Wrapper Feature Selection Please refer to the Jupyter notebook (Example.ipynb) for an example of using t

9 Dec 28, 2022
Official repo for AutoInt: Automatic Integration for Fast Neural Volume Rendering in CVPR 2021

AutoInt: Automatic Integration for Fast Neural Volume Rendering CVPR 2021 Project Page | Video | Paper PyTorch implementation of automatic integration

Stanford Computational Imaging Lab 149 Dec 22, 2022
Super Pix Adv - Offical implemention of Robust Superpixel-Guided Attentional Adversarial Attack (CVPR2020)

Super_Pix_Adv Offical implemention of Robust Superpixel-Guided Attentional Adver

DLight 8 Oct 26, 2022
SafePicking: Learning Safe Object Extraction via Object-Level Mapping, ICRA 2022

SafePicking Learning Safe Object Extraction via Object-Level Mapping Kentaro Wad

Kentaro Wada 49 Oct 24, 2022
Language-Driven Semantic Segmentation

Language-driven Semantic Segmentation (LSeg) The repo contains official PyTorch Implementation of paper Language-driven Semantic Segmentation. Authors

Intelligent Systems Lab Org 416 Jan 03, 2023
Photographic Image Synthesis with Cascaded Refinement Networks - Pytorch Implementation

Photographic Image Synthesis with Cascaded Refinement Networks-Pytorch (https://arxiv.org/abs/1707.09405) This is a Pytorch implementation of cascaded

Soumya Tripathy 63 Mar 27, 2022
Python library for tracking human heads with FLAME (a 3D morphable head model)

Video Head Tracker 3D tracking library for human heads based on FLAME (a 3D morphable head model). The tracking algorithm is inspired by face2face. It

61 Dec 25, 2022
OpenCVのGrabCut()を利用したセマンティックセグメンテーション向けアノテーションツール(Annotation tool using GrabCut() of OpenCV. It can be used to create datasets for semantic segmentation.)

[Japanese/English] GrabCut-Annotation-Tool GrabCut-Annotation-Tool.mp4 OpenCVのGrabCut()を利用したアノテーションツールです。 セマンティックセグメンテーション向けのデータセット作成にご使用いただけます。 ※Grab

KazuhitoTakahashi 30 Nov 18, 2022
Code to reproduce the experiments from our NeurIPS 2021 paper " The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective"

Code To run: python runner.py new --save SAVE_NAME --data PATH_TO_DATA_DIR --dataset DATASET --model model_name [options] --n 1000 - train - t

Geoff Pleiss 5 Dec 12, 2022
Winners of DrivenData's Overhead Geopose Challenge

Winners of DrivenData's Overhead Geopose Challenge

DrivenData 22 Aug 04, 2022
Heat transfer problemas solved using python

heat-transfer Heat transfer problems solved using python isolation-convection.py compares the temperature distribution on the problem as shown in the

2 Nov 14, 2021
A pytorch implementation of MBNET: MOS PREDICTION FOR SYNTHESIZED SPEECH WITH MEAN-BIAS NETWORK

Pytorch-MBNet A pytorch implementation of MBNET: MOS PREDICTION FOR SYNTHESIZED SPEECH WITH MEAN-BIAS NETWORK Training To train a new model, please ru

46 Dec 28, 2022
Surrogate- and Invariance-Boosted Contrastive Learning (SIB-CL)

Surrogate- and Invariance-Boosted Contrastive Learning (SIB-CL) This repository contains all source code used to generate the results in the article "

Charlotte Loh 3 Jul 23, 2022
Lightweight library to build and train neural networks in Theano

Lasagne Lasagne is a lightweight library to build and train neural networks in Theano. Its main features are: Supports feed-forward networks such as C

Lasagne 3.8k Dec 29, 2022
Jittor implementation of PCT:Point Cloud Transformer

PCT: Point Cloud Transformer This is a Jittor implementation of PCT: Point Cloud Transformer.

MenghaoGuo 547 Jan 03, 2023