Month: September 2024

# Jump To:

• Introduction & Conception
• Piano Game Concept
• Actual Game Concept
  • Complex part & Risk Reduction

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# Introduction

Hey everyone! 👋

Like probably everyone else on the entire planet, I wasn’t really sure what to do for my Intro to IM Midterm Project at NYUAD (ok fine, maybe not everyone else) (fun fact, I’m still not certain 😂😭). I was deliberating whether to stick with traditional input devices (mouse and keyboard), or go for some interesting new (like the classic and probably cliche, face or body tracking). Unfortunately, I don’t own a guitar like Pi Ko, so I guess I’ll have to stick with something traditional 😅 (now that I think about it, I could use a piano, wait a moment…)

 

# Piano Game Concept

So the keys of the piano (but optionally also the keyboard, allowing everyone to play it) control the height of the bars, and you have to get a ball that drops out of the start area into the goal, potentially avoiding obstacles and overcoming bad physics, all the while automatically producing (probably ear piercing) music random garble of sounds. That’s a win-win-win!

Ok, this was a joke. Now back to the actual concept I wanted to talk about 😅 (though to be honest, this seems more original and fun ._.)

 

# Actual Game Concept

You know those outlines they have of people (like at crime scenes, but don’t think about that)?

Or those ones where a cartoon character goes through a wall?

 

Well, what if you had to make the correct pose to fit through a series of holes and avoid hitting the wall? That’s the essence of my idea. I thought about it while thinking on how I could utilise face/body tracking (which probably shows 😅), and which is exactly the wrong approach (you’re supposed to first have an issue/idea, then think about how to solve it, not try to find a use case for a certain techonology, that’s like a solution in search of a problem). Also, this idea is simple and obvious enough that while I haven’t seen it yet, it very well might already be a real thing. Still, I find the idea quite charming, especially as I envision it on a large screen, with people frantically and hilariously jumping around positions. I will also include a “laptop mode”, where the program only shows upper body cutouts, in order to make it accessible and playable on laptops too (where it would be hard to get the distance required for the camera to see the full body, while still allowing you to comfortably see the screen).

It should not come as a surprise then, that I plan to use body tracking to be able to control the motion of the character. Of course, it would be a gargantuan task to implement a decent body tracking solution from scratch (something more for a final year’s or even PhD’s research project than an Intro to IM midterm), so I will use a pre-existing library to handle the detection for me, mainly movenet (probably through ml5.js), a fast and accurate pose detection model by Google.

As I was thinking about this a bit, I began thinking that maybe this could be about a ninja’s training. You enter the secret facility (the interface is hopefully interactable through hand gestures, similar to the Xbox with kinect, if I have enough time), and then have to undergo the “lightning quick attack & defense poses” (or something), as part of your training.

 

## Complex part & Risk Reduction

As part of our midterm progress blog post, we have to identify the most complex/frightening part, and do something to tackle and reduce that risk. For this project, it is obviously the body tracking, so I created a new sketch to test out the library, and coded together a simple barebones concept to ensure the tracking works, and if I can reliably assess whether a person is in the correct pose. In addition to the pose detection, it also displays a skeleton of the person (of the detected points), and I made it slightly show the webcam’s video feed (both to help the player adjust their position). Also, it shows a (faint) guide to the target pose.

Mini rant:

I first made an image for the target pose, but I’m still somehow unable to upload anything to p5, despite trying with different browsers, at different times, and even different accounts, since the start of the semester (yep, I haven’t been able to upload anything at all). Luckily in this case, it was just a simple image, so I drew it manually with p5.js (which is better in fact, since the image automatically updates if I change the target pose), but still, this is extremely annoying and limiting. If you know any solutions, please let me know.

 

Try and see if you can match the pose!

 

(Hint: It tests for the shoulders, elbows, and wrists. Also, try moving back and forth if the frame doesn’t fit, and try changing the lighting if it isn’t detecting you properly (ignore the flickering, that’s a known issue))

It works! Something I don’t like is the constant flickering, but I think I might be able to mostly solve that (at the expense of slower update times, by using a moving/sliding average), so I would consider this a success!

Hey there! 👋

I was reading this great article about Computer Vision for Artists and Designers, which mainly talks a bit about the history of computer vision, interactive media using computer vision, and some basic computer vision techniques.

It made me think about an interesting comparison, of the difference between our vision, and that of a computer’s. While the structural and architectural differences are obvious (biological vs technological), a more interesting comparison point lies in the processing. Video, as the article mentioned, doesn’t contain any inherent, easily understood meanings. Instead, in both cases, it is up to the processing to extract key information and figure out the required details. While such a task is usually easy for our brain, trivial in fact, is an incredibly difficult problem to overcome, especially from scratch. It might seem easy to us, because our brain is one the most complex things in the universe, and also had a huge head start 😂, allowing it to develop specialised pathways and processes capable of carrying out this operation in a breeze, which is understandable as vision is an extremely important tool in our survival. Computer vision meanwhile, is starting in this gauntlet of a task, nearly from scratch. While we do not have to evolve biological structures and compete on our survival, there is still a very real challenge, though thankfully, we can progress on this frontier much quicker than evolution or nature could (that big brain of ours coming in handy again. Wait, if you think about it, we are using our eyes and brain, to replace, our eyes and brain… and our entire body and self, in fact).

There are several methods of implementing different aspects of computer visions. Some basic ones, mentioned in the article, include detecting motion (by comparing the difference in the current frame with the previous one), detecting presence (by comparing the difference in the current frame with one of the background), brightness thresholding (just checking if a pixel is lighter or darker than a certain threshold), and rudimentary object tracking (usually by finding the brightest pixel in the frame), though many other basic techniques also exist (eg. such as recognising what a picture is by comparing against a stored collection of images). These basic implementations however cannot stand much alternation in their expected environment, and even something relatively simple (like the background changing colour, perhaps by a shadow being cast) would render them ineffective. Also, trying to implement a task as vast as computer vision by precise, human defined algorithms, is extremely hard (I know all algorithms are technically precisely defined, so what I mean by this is that we have specifically coded in behaviours and processes).

A far more successful approach (like in many other fields from aerodynamics to energy efficiency), has been to try and somewhat copy what nature does (though on a simpler scale). “Recent” approaches like neural networks, and other machine learning techniques, have begun far outperforming anything a precisely defined algorithm could do, on most real world tasks. The simplest structure of a neural network, is to have neurones, or nodes, connected to other nodes, and the value is simply modified as it travels from node to node (vast oversimplifying), mimicking the model of the neurons in a brain (though in a much more basic representation). The beauty of such approaches is that they leave the specifics undefined, allowing the model and training process to improve itself, automatically choosing a somewhat good selection of processes to extract meaningful information (when done right). Of course, this doesn’t mean that this is easy, or that one a neural network is made, all the problems can be solved by simply feeding it enough data – innovation in the architecture of the neural networks themselves (e.g. the introduction of U-nets, LTSM, transformers, etc) and surrounding ecosystem must also happen – but it does allow a different way of doing things, which has so far yielded fruitful results (checkout any of the vast number of computer vision libraries, including PoseNet, YOLO, and probably most importantly, OpenCV).

Though the advancements in computer vision are coming ever faster, and many people around the globe dream about being able to control things with just their body and vision (probably in no small part due to movies showing cool futuristic tech), the use of computer vision, in anything, is still a heated discussion. On one hand, widely implementing it could provide alternative pathways to accomplish tasks for many people who might otherwise not be able to do it, increasing accessibility, and it also enables several use cases which before were never even thought to be remotely possible (and also, it’s just cool!). On the other hand however, as with any technology, we have to acknowledge that the vast capabilities of such a system could also be used for nefarious purposes. In fact, governments and corporations are already misusing the capabilities to carry out dystopian practices on an unprecedented scale, widely being in mass surveillance, targeted tracking, selling user info, avoiding responsibilites (eg. car manufacturers on warranty claims), and so much more, all for power and profit (you might think I’m exaggerating a little, but no, look it up. The rabbit hole goes extremely deep (and I’d be happy to chat about it 🙂 )). As a certain spidey’s uncle once said, “With great power comes great responsibility”, and currently, we can’t be sure that those with that power won’t abuse it, especially as they have already routinely been.

With such a conundrum of implications and possibilities, computer vision’s use in interactive art is no doubt a little damper, with it predominantly being featured in places where it is easily and visibly stopped/escaped, such as galleries and public (but not too public) and private installations, and apps and websites users trust, vs throughout every aspect of our lives (though it is undoubtedly hard to fully trust an app/website, and that trust can be easily broken, though of course, not everyone is as weary of these technologies).

Concept :

I’ve always found the brain fascinating, especially how neurons fire and communicate. It’s like this intricate dance of connections that sparks thoughts and actions. Interestingly, voices also make our neurons fire. So, I thought, why not combine that with a game I love? I decided to merge the concept of neuron firing with the mechanics of the classic 2048 game.

Instead of numbers merging, it’s all about neurons connecting and lighting up, almost like a visual representation of a neural network in action. It’s exciting to see how each move can mimic real-life brain activity. The main goal is to keep merging neurons to reach the ultimate neuron—represented as a “Super Neuron”—while continuously creating connections to maintain an active and vibrant neural network. Players can aim for high scores, challenging themselves to beat their previous records or compete with others.

 

Game Design: 

The main goal of the game is to keep building neuron connections. Among all the neuron like particles, there are some target particles (super neuron) that are shown as neuron spark. Player needs to click on the glowing/target particle to create a connection with sparking neuron and the neurons near the area. The neurons merges and more connections keeps building as apart of the simulation. If the player is able to make 05 connections, the player will pass the level.

Codes I’m Proud of: 

Still, the code is in a beginning level and requires lots of modifications to finish the game. The first thing I liked is how the neurons look like which I created using particles as function.

// Start simulation with neurons
function startSimulation() {
  background(0);
  orbitControl();  // Allow mouse control to rotate

  // Analyze the audio amplitude and spectrum
  let spectrum = fft.analyze();
  let level = amp.getLevel();

  // Set lighting
  ambientLight(20, 20, 30);
  pointLight(255, 255, 255, 0, 0, 300);

  // Slowly rotate the scene
  rotateY(frameCount * 0.001);
  rotateX(frameCount * 0.0015);

  // Draw neurons (with sparkle effect)
  for (let i = 0; i < particles.length; i++) {
    let p = particles[i];
    p.move(level);
    p.display();
    
    // Draw lines between nearby particles (neurons)
    for (let j = i + 1; j < particles.length; j++) {
      let d = dist(p.pos.x, p.pos.y, p.pos.z, particles[j].pos.x, particles[j].pos.y, particles[j].pos.z);
      if (d < 120) {
        strokeWeight(map(d, 0, 120, 4, 0.1));
        stroke(150, 150, 255);
        line(p.pos.x, p.pos.y, p.pos.z, particles[j].pos.x, particles[j].pos.y, particles[j].pos.z);
      }
    }
  }

  // Draw the target particle (glowing neuron)
  targetParticle.move();
  targetParticle.display();
  
  // Handle interactions with the target particle
  if (mouseIsPressed) {
    let d = dist(targetParticle.pos.x, targetParticle.pos.y, targetParticle.pos.z, mouseX - width / 2, mouseY - height / 2, 0);
    if (d < 50) {
      fireNeurons();
      connectToNearestNeuron();  // Connect the glowing neuron to the nearest neuron
      score++;  // Increase score when a neuron is clicked
    }
  }

I also tried to make the particles change positions according to the audio amplitude.

fft = new p5.FFT();
amp = new p5.Amplitude();

// Analyze the audio amplitude and spectrum
let spectrum = fft.analyze();
let level = amp.getLevel();

I tried to implement another  cool feature that creates dynamic connections between neurons when they come near each other. This mimics real neural networks and adds a layer of interaction. But, the code might have some flaws.

// Draw lines between nearby particles (neurons)
for (let j = i + 1; j < particles.length; j++) {
  let d = dist(p.pos.x, p.pos.y, p.pos.z, particles[j].pos.x, particles[j].pos.y, particles[j].pos.z);
  if (d < 120) {
    strokeWeight(map(d, 0, 120, 4, 0.1));
    stroke(150, 150, 255);
    line(p.pos.x, p.pos.y, p.pos.z, particles[j].pos.x, particles[j].pos.y, particles[j].pos.z);
  }
}

P5.js Sketch:

Future Works:

• Need to find a proper background with the name visible for players.
• Need to add descriptions on the Read part and fix the settings on the Settings Page.
• Regarding the game design part, it requires to fix the game mechanics as the game flow is not implemented yet.
• Need to monitor the performance and use techniques like frame rate. 
• Need to work on the sound. Create a sound experience that reacts dynamically to what’s happening in the game. For example, as neurons connect, the music could change in tempo or complexity

CONCEPT:

Reading, as typical as it sounds, really and truly does fuel my creativity. I have always been keen on Shakespeare, particularly his tragedies. For this project, I want to explore how to toy with the human eye. In Shakespearean tragedies, the protagonists experience some sort of downfall that is indicated through the beginning of experiencing hallucinations. It is typically catalyzed due to paranoia and guilt – or a general feeling of loss of control over a situation. Macbeth is one of those characters. The Tragedy of Macbeth stands out because it is a lesson on arrogance and ambition – and how quickly the human spirit can be broken from guilt and ambition. Macbeth and his wife, Lady Macbeth, experience a series of hallucinations as they attempt to wrap their minds around the notion of regicide. Therefore, this project will have the user attempt to help them through these hallucinations.

EXPLAIN:

There are three hallucinations in Macbeth: A Dagger that Macbeth sees floating in the air that he cannot seem to catch; Banquo’s ghost (Macbeth’s friend he murdered), and blood on Lady Macbeth’s hands. These hallucinations are a manifestation of their guilt. For this project, I have chosen to leave out the hallucination of Banquo, as I would like to do a more stable approach of focusing and detailing and balancing the hallucinations between the husband and wife, the Macbeths. The Dagger Scene, and the Bloody Spot Scene are going to be divided into two levels or scenes.

SCENES:

The dagger level will have a floating hand (Macbeth) attempt to catch a floating dagger, but it will always seem to evade his hand, essentially driving him mad. This is inspired by the monologue he has, “Is this a dagger which I see before me?/The handle toward my hand?/ Come, let me clutch the/  I have thee not, and yet/ I see thee still.”. Here, he is basically saying that he sees this dagger, but as he tries to capture it in his hand, he cannot. I would add a speed variable – so that at moments the dagger will slow down, giving the user the opportunity to catch it. The purpose of this game overall  is not to win, but to simply understand Macbeth and the play a little better. 

For Lady Macbeth’s hallucination — the Bloody Spot level — the user will engage in a different type of interaction that reflects her overwhelming guilt. In this scene, the user will control Lady Macbeth’s hands as they attempt to wash away the bloodstains that symbolize her remorse. The gameplay will involve blood spots appearing randomly on the screen, and the user will need to “wash” these spots by moving Lady Macbeth’s hands over them. As the user succeeds in removing one spot, new ones will emerge, creating an endless cycle that mirrors her inability to escape her guilt. This mechanic emphasizes the futility of her actions and the psychological torment she experiences.

The purpose of the user interaction is to highlight the internal struggles and frailty of the characters. 

IMPLEMENTATION:

To effectively implement these ideas in code, I plan to create several classes: Dagger, Hand, and BloodSpot. Each class will encapsulate properties such as position, speed, and visibility. The functions will include moveDagger(), which updates the dagger’s position and speed; checkCollision(), which detects proximity between the hand and dagger; generateBloodSpots(), which creates new blood spots on the screen; and washBloodSpot(), which reduces the opacity of blood spots when interacted with. 

However, I anticipate several challenges throughout this process. The dagger’s movement algorithm must be realistic yet unpredictable enough to convey Macbeth’s frustration. 

Creating a sense of futility in gameplay will also be essential. Balancing difficulty is key; while users should feel challenged, they must also understand that catching the dagger is ultimately impossible. Gradually increasing difficulty or ensuring that new blood spots continuously appear will reinforce this theme. Lastly, integrating atmospheric sounds or character voices using the p5.sound library could enhance immersion and emotional impact.

To minimize risks associated with these challenges, I plan to implement the dagger movement algorithm first and test various approaches to ensure it feels engaging yet frustrating. Creating a prototype for the blood spot washing mechanic will help determine its intuitiveness early on. 

CODE:

In terms of my current code, I also decided to add two menus – one at the start and one at the end. The one at the start introduces the user to Macbeth. I wanted to make this extremely engaging. Although the final interface and design for the menu is not done yet, I wanted to implement sound almost immediately. The first speakers – the female voices — are actually from Macbeth. It is a prominent dialogue in which they, the witches, converse with Macbeth and let him know of a prophecy – that he will be king. However, there is already a king. So for Macbeth to be king, that means he would have to kill him. I wanted to create an eerie and uncomfortable experience — the whole time, I want users to be on their toes. 

 

However, I am most proud of figuring out how to navigate through different levels. It seemed like a no-brainer, but it took me time to figure it out.

function mouseClicked() {
  if (currentScene == 'menu'){
    currentScene = 'dagger';
  }
  else if (currentScene === 'dagger') {
    currentScene = 'bloodySpot';
  } 
  else if (currentScene == 'bloodySpot') {
    currentScene = 'closing'
  }
}

I have a lot left to accomplish, but overall I am looking forward to doing something unique.

 

 

Reflecting on the reading “Computer Vision for Artists and Designers,” it’s clear that computer vision techniques have found a meaningful place in the world of art. The text highlights a variety of artistic applications, showcasing everything from abstract creations to works addressing sociopolitical themes. Golan Levin does a great job of illustrating this diversity by mentioning numerous artists and their projects that incorporate computer vision, which really underscores the potential of this medium.

When it comes to how computer vision differs from human vision, the distinctions are pretty significant. While our visual system is adaptable and can interpret a wide range of visual stimuli effortlessly, computer vision is limited by its programming and the specific conditions it can handle. Unlike us, computers can’t easily generalize or grasp context, which are essential parts of how we see. Lastly, the potential for tracking and surveillance through computer vision has a mixed impact on interactive art. On one hand, it opens up new avenues for artists to engage audiences and create immersive experiences. On the other hand, it raises important privacy concerns and the risk of misuse in monitoring behavior. In the end, artists need to find a careful balance, using computer vision to explore their creativity while also being aware of ethical concerns.

Concept: For my midterm project, I created an interactive zoo where users can virtually visit different animal enclosures and experience their sounds in a visually engaging way. The idea behind the project is to give users a simple, intuitive interface to explore various animal exhibits within a zoo environment. The user can click on an enclosure to hear the animal and see a more detailed visual of it.

Frightening Aspect: One of the key challenges I faced was managing sound playback. If I don’t handle this properly, sounds could overlap or continue playing when I switch enclosures which would make the experience messy.

Risk Management:

• I added stopSound to stop the sound when I leave an enclosure. This makes sure that as soon as I press ‘R’, the sound for the current enclosure stops, avoiding any overlap with future sounds.
• When I press ‘R’ to return to the zoo home screen, I use a loop to stop all the sounds from the enclosures. This guarantees that no sound continues playing when I leave an enclosure.

Reading through the passage by Golan Levin and others, I was re-introduced into the world of computer vision without being overwhelmed by technical jargon. As someone who used to get in touch with CV only on a library-resorting basic, aka. using OpenCV in Python, rediscovering some rudimentary algorithms behind it from a perspective of technology in arts brought me some really fascinating ideas compared to the old-school path-finding tasks.

The paper acknowledges how CV has traditionally been the realm of expert researchers and applications limited to military and law enforcement (and yes, of course, teenagers’ robotics tournaments).  With advancements in software development tools, the rise of open-source communities, and the affordability of digital video hardware, CV is no longer an exclusive playground. Artists and designers are now empowered to experiment and innovate, integrating vision-based interactions into their creative projects with relative ease.

A particularly insightful section for me is to emphasize the importance of the physical environment in enhancing the performance of CV systems. The authors argue that optimizing lighting conditions, using retroreflective materials, and selecting appropriate camera lenses can significantly improve the accuracy and reliability of vision algorithms. In my own projects that utilized webcam and process on the video pixels, although the ‘algorithm’ is simply resorting to the grayscale conversion, it is already noticeable to see how the physical environment affects the outcome. The collaboration between software and the physical setup underscores a holistic approach to designing interactive media, where both technological and artistic considerations play crucial roles.

Reflecting on the questions posed by the paper, it becomes clear how CV fundamentally differs from human vision. While humans effortlessly interpret context, recognize patterns, and infuse semantic meaning into visual data, CV relies on meticulously crafted algorithms to process pixel information without inherent understanding, creating both opportunities and challenges for us. By all means, the opportunities lie in the possibility of fine-tuning, altering, and utilizing what the computer has ‘seen’ with algorithms (in other words, to ‘think’ after the ‘listening’ and before ‘speaking’) while it is hard to change the optical structure of human vision. On the other hand, this ‘thought-precedes-realization’ approach in harnessing CV could result in hindering the imagination of artists as the fine line between envisioning and then realizing and the more intuitive manner of mixing the creation and designing at the same time can be easily blurred by lines of codes.

Besides, this powerful capability also introduces ethical considerations, especially when used in interactive art. The capacity for tracking and surveillance can enhance the immersive quality of art installations, making them more responsive and engaging. Yet, it also raises concerns about privacy and consent. For web-based instances like the games I’m currently working with, it is easy and necessary to ask for permission from the user, while installations and broader observational systems could skip the users’ first-hand consent. How do we balance the creative potential of CV with the need to respect individual autonomy and privacy? These questions are crucial as artists and technologists continue to push the boundaries of what interactive art can achieve.

 

Norman’s exploration of everyday objects like doors, switches, and scissors turns the mundane into a reflection of hidden complexities and subtle interactions. As I dove into his ideas about affordances and signifiers, I began seeing my surroundings in a different light. The handle of my coffee mug, the curve of my desk lamp, and even the layout of my keyboard buttons weren’t just functional anymore; they became silent storytellers, each with their own design narrative.

As a left-handed person, Norman’s ideas on affordances and signifiers took on an even deeper meaning for me. I’ve often struggled with objects clearly not designed with me in mind can openers, scissors, and the constant smudging when I write. These frustrations were aptly highlighted in Norman’s push for more inclusive, human-centered design. His arguments about making design accessible to everyone, not just the majority, struck me on a personal level.

The concept of discoverability really stood out during the time I spent reading the chapter. I’ve often found myself baffled by unfamiliar appliances, caught between curiosity and confusion. Norman helped explain why some objects feel intuitive and welcoming, while others seem to thrive on leaving us puzzled. I think back to my experiences with the university printers, with poorly labelled buttons, and an digital interface buried in endless menus. Something as simple as scanning a document became a frustrating guessing game, all because there were no clear signifiers to guide me. In contrast, a well-designed printer would have had intuitive, clearly marked buttons or icons, making the process straightforward and stress free. It was a perfect illustration of Norman’s design principles or lack thereof.

Norman’s “paradox of technology” also resonated with me. As our devices become more advanced, they often become harder to understand. It’s ironic how progress can make us feel more disconnected. I think about my grandmother struggling with her new smartphone, a device that’s supposed to make life easier but instead leaves her feeling lost. It’s a clear example of how our pursuit of innovation can create barriers instead of breaking them down.

The emphasis on feedback also struck me as important. In our world of touchscreens and voice commands, clear feedback is more necessary than ever. I find myself missing the click of a button – the small but essential signal that something has actually happened. Without feedback, it’s easy to feel disconnected from the technology I am using.

Norman’s idea of conceptual models made me reflect on my own assumptions. How often do I approach a new device or system with a flawed understanding of how it works? It’s humbling to realize that many of my mental models are incomplete, and that much of what I think I know is just a simplified version of reality.

After finishing the chapter, I felt both inspired and responsible. Norman reveals how design influences every interaction we have, and as someone studying interactive media, I realize I might one day contribute to shaping these interactions. Norman’s call for human-centered design isn’t just about convenience – it’s about honoring the complexity and diversity of the human experience.

In the end, his message reminds us that between people and objects lies a world of potential – and that good design is about more than just solving problems. It’s about understanding what it means to be human.

MIDTERM IDEA

My idea midterm is to create a short game. One of my favorite (unfinished) games is It Takes Two and it involves the cooperation of two players in order to move on to the next level/ chapter of the game. Though my game will likely not be as intricate as the storyline in It Takes Two, I wanted to draw upon my experiences and create a simply two player corporation minigame.

For my project, I want to have a pattern game with one user using the WASD, one using the arrow keys, and communal SPACE key.  On the screen there will be a combination of the WASD, arrow, and space keys which the two players must work together to enter in the right order to defeat some boss. I definitely still need to work on the storyline/ goal of the game, but I wanted to figure out the basic mechanism and gameplay for my project as a starting point for my midterm project.

GREATEST FEARS 

The biggest obstacles I will face in this project is making the project simple, yet complex at the same time. I believe I will have an issue of doing more than I can in the time I am allotted along with adding an unnecessary amount of detail to my project. For example, with the graphics of the game, I want to include frame-by-frame animations and movements of two characters that represents each player. However realistically, animation takes forever, and I do not unfortunately do not have the free time to draw/ create characters for the complex movements I wish.

While I do fear the images/ shapes would not satisfy what I am looking for, I hope to minimize my fears by designing and animating simple character designs that are already available in the p5 library. I feel if I could make a self-portrait of myself, I hope I would be able to small version of that assignment. In addition, I hope to create a lot of test cases and examine online examples to gauge how others were able to create their games.

MY PROGRESS

I feel when I start projects, I always dive in without much of a plan and either end up scraping the idea because I forgot what the objectives were or scraping up ideas as I work. As such, I decided to make a mood/ idea board that laid out almost a step-by-step process for what I want to implement into my midterm project, so I don’t get overwhelmed or forget to include something I wanted. Below I have included my vision for my game project and list out the components I want (as time permits).

In the sketch, you can see there’s two players that are dancing, but the controls are bounded to the player’s control either the arrow keys or the WASD keys. Together, they’ll have to work together and coordinate which creature they are looking to target first and eliminate. Otherwise, if the creatures reach them within a certain distance a health point will be lost. I do feel I haven’t fully decided on the setup of the players, but I think once it is tested, I can then also decide if I need to make any changes.

Vision Board For My Project

In addition, I have also included the external influences that are inspiring my game. Namely again, the co-op game It Takes Two, arcade games like Space Invader, and character designs of cartoons/ Google Doodles.

External Influences/ References