Generative AI Processes for 2D Platformer Game Character Design and Animation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AI has the potential to revolutionize the time-consuming and technically complex process of 2D animation production. This paper specifically focuses on creating 2D character animations for platformer games using AI. While AI has made significant contributions to video animations, its application in 2D gaming animation is largely unexplored. Existing AI applications for animation mostly target video animations and lack effective control over randomness. Therefore, this paper explores the role of generative AI in 2D gaming animation, from character design to full animation. Software tools like ChatGPT, Midjourney, Stable Diffusion, and Unity are used to streamline the production process. The research aims to investigate the feasibility and potential of generative AI, with a focus on controlling randomness. By leveraging the unique features of each software, the study aims to enhance the production of 2D game animations. The final output will be an animated character in the “Idle” state, showcasing the potential of generative AI in 2D gaming animation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it