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Found 7 results

  1. Hello! Previous year in my job we implemented a HDR output (as in HDR10 / BT.2020 / ST.2084 PQ back-buffers) on one of our games on the consoles, which do support HDR10 over HDMI. The HDR compatible hardware (monitors, televisions) has already been around for a year, with varying quality. I wonder if there's already HDR-HW output exposed in the PC drivers? Windows 10? Vulkan? DX 11? DX 12? Which vendors? For those unfamiliar, I'm talking about outputting HDR signal to HDR hardware (using r10g10b10a2_unorm + PQ backbuffers, or better). Thanks, .P
  2. Hi everybody, i'm ready to announce that Xilvan Design build games since 1993: The Labyrinth. v3.07. Soul of Sphere Platinum v2.75. Alako the Koala. v1.01 Age of Dreams v2.50. Lights of Dreams IV v7.97. Candy World II v8.37. Candy Racing Cup v2.75. Candy World Adventures v5.17. Candy to the Rescue IV v5.97. Candy in Space III v5.47. Candy's Space Adventures v15.75. Candy's Space Mysteries II v6.27. Two updates since last edit: - Enhanced the Space, Missions & Sceneries in Candy's Space Adventures. - Verified bonus, options, maps & more tricks in Candy's Space Mysteries II. - Reverified all of our old games like Candy World II, Candy to the Rescue IV, Candy World Adventures IV. - Maybe a Candy in Space III quest will be available soon. - We are planning Lights of Dreams V. - Worked on the "Level Select Screen" in Candy World Adventures IV. - We are actually debuging all of our games since now then. They are available for download on my website: - Xilvan Design Websites - One new link are now available: a OneDrive link. But, there is still a Google Drive & old CNET.download.com link. Hope you'll appreciate ! If you want to watch the videos of our games: - My youtube Channel - Please, Subscribe to my channel for more infos about our new releases. Friendly, Xylvan, Xilvan Design.
  3. Animating characters is a pain, right? Especially those four-legged monsters! This year, we will be presenting our recent research on quadruped animation and character control at the SIGGRAPH 2018 in Vancouver. The system can produce natural animations from real motion data using artificial neural networks. Our system is implemented in the Unity 3D engine and trained with TensorFlow. If you are curious about such things, have a look at this:
  4. I am about to start a PhD that will investigate ways of replicating creativity in the AI systems of simulated people in virtual environments. I will research which psychology theories and models to use in order to achieve this, with a focus on creative problem solving. The aim of this project is to create virtual characters and NPCs that can create new solutions to challenges, even if they have never encountered these before. This would mean that not every possible action or outcome would need to be coded for, so less development resources are required. Players would encounter virtual people that are not bound by rigid patterns of pre-scripted behaviour, increasing the replay value and lifespan of games, and the accuracy of simulations. I am looking for companies or organisations that would be interested in working with me on my PhD, and I think computer games companies might be the most likely. I am trying to think of ways in which this new AI system might benefit games companies, or improvements and new types of games that might be possible. I am on this forum to ask for your thoughts and suggestions please, so I can approach games companies with some examples. Thank you for your time and interest.
  5. Gourav Mishra

    R&D chatbot

    Please help in completing the code. I am unable to use defined function import sys import nltk import random from nltk.tokenize import word_tokenize,sent_tokenize GREETING_KEYWORDS = ("hello", "hi", "greetings", "sup", "what's up",) GREETING_RESPONSES = ["'sup bro", "hey", "*nods*", "hey you get my snap?"] User_input = input ("User said:") type (User_input) def check_for_greeting(sentence): """If any of the words in the user's input was a greeting, return a greeting response""" words = word_tokenize (sentence) if words in GREETING_KEYWORDS: print(random.choice(GREETING_RESPONSES)) return; check_for_greeting(sentence = User_input )
  6. thecheeselover

    Zone generation

    I have integrated the zone separation with my implementation of the Marching Cubes algorithm. Now I have been working on zone generation. A level is separated in the following way : Shrink the zone map to exactly fit an integer number of Chunk2Ds, which are of 32² m². For each Chunk2D, analyse all zones inside its boundaries and determine all possible heights for Chunk3Ds, which are of 32³ m³. Imagine this as a three dimensional array as an hash map : we are trying to figure out all keys for Chunk3Ds for a given Chunk2D. Create and generate a Chunk3D for each height found. Execute the Marching Cubes algorithm to assemble the geometry for each Chunk3D. In our game, we want levels to look like and feel like a certain world. The first world we are creating is the savanna. Even though each Chunk3D is generated using 3D noise, I made a noise module to map 3D noises into the 2D to able to apply 2D perturbation to the terrain. I also tried some funkier procedural noises : An arch! The important thing with procedural generation, it's to have a certain level of control over it. With the new zone division system, I have achieved a minimum on that path for my game.
  7. thecheeselover

    Zone division

    A friend and I are making a rogue-lite retro procedural game. As in many procedural rogue-lite games, it will have rooms to complete but also the notion of zones. The difference between a zone and a room is that a zone is open air whilst a room is not. Rooms are connected mainly by corridors while zones are mostly naturally connected / separated by rivers and mountains. Because we want levels with zones to be generated, we need to tame the beast that is procedural generation. How can we generate each zone itself and also clearly divide them? Until now, I had only been using the Java noise library called Joise, which is the Java community port of JTippetts' Accidental Noise Library. I needed the zone data to be generated with basis function modules, i.e. Perlin noise, but in contrast I needed a more structured approach for the zone division. Joise library does have a cell noise module that is a Worley noise. It looks like this depending on its 4 parameters (1, 0, 0, 0) : Using math modules, I was able to morph that noise into something that looks like a Voronoi diagram. Here's what a Voronoi diagram should look like (never mind the colors, the important parts are the cell edges and the cell centers) : A more aesthetic version : The Worley noise that I had morphed into a Voronoi-like diagram did not include the cell centers, did not include metadata about the edges and was not enough deterministic in a sense that sometimes, the edges would around 60 pixels large. I then searched for a Java Voronoi library and found this one called Voronoi-Java. With this, I was able to generate simple Voronoi diagrams : Relaxed : 1 iteration Relaxed : 2 iterations The relaxation concept is actually the Lloyd's algorithm fortunately included within the library. Now how can I make that diagram respect my level generation mechanics? Well, if we can limit an approximated number of cells within a certain resolution, that would be a good start. The biggest problem here, is that the relaxation reduces the number of cells within a restricted resolution (contrary to the global resolution) and so we need to keep that in mind. To do that, I define a constant for the total number of sites / cells. Here's my code : private Voronoi createVoronoiDiagram(int resolution) { Random random = new Random(); Stream<Point> gen = Stream.generate(() -> new Point(random.nextDouble() * resolution, random.nextDouble() * resolution)); return new Voronoi(gen.limit(VORONOI_SITE_COUNT).collect(Collectors.toList())).relax().relax().relax(); } A brief pseudo-code of the algorithm would be the following : Create the Voronoi diagram Find the centermost zone Selects X number of zones while there are zones that respect the selection criteria Draw the border map Draw the smoothed border map The selection criteria is applied for each edge that is connected only to one selected zone. Here's the selection criteria : Is connected to a closed zone, i.e. that all its edges form a polygon Does have two vertices Is inclusively in the resolution's boundaries Here's the result of a drawn border map! In this graph, I have a restricted number of cells that follow multiple criteria and I know each edge and each cell center point. To draw the smoothed border map, the following actions must be taken : emit colors from already drawn pixels and then apply a gaussian blur. Personally, I use the JH Labs Java Image Filters library for the gaussian blur. With color emission only : With color emission and a gaussian blur : You may ask yourself why have we created a smoothed border map? There's a simple reason for this, which is that we want the borders to be gradual instead of abrupt. Let's say we want rivers or streams between zones. This gradual border will allow us to progressively increase the depth of the river and making it look more natural in contrast with the adjacent zones. All that's left is to flood each selected cell and apply that to a zone map.
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