On Linking Level Segments

• URL: http://arxiv.org/abs/2203.05057v1
• Date: Wed, 9 Mar 2022 21:32:41 GMT
• Title: On Linking Level Segments
• Authors: Colan Biemer and Seth Cooper
• Abstract summary: We present a Markov chain and a tree search algorithm that finds a link between two level segments. This method reliably finds links between segments and is customizable to meet a designer's needs.
• Score: 3.04585143845864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: An increasingly common area of study in procedural content generation is the creation of level segments: short pieces that can be used to form larger levels. Previous work has used basic concatenation to form these larger levels. However, even if the segments themselves are completable and well-formed, concatenation can fail to produce levels that are completable and can cause broken in-game structures (e.g. malformed pipes in Mario). We show this with three tile-based games: a side-scrolling platformer, a vertical platformer, and a top-down roguelike. Additionally, we present a Markov chain and a tree search algorithm that finds a link between two level segments, which uses filters to ensure completability and unbroken in-game structures in the linked segments. We further show that these links work well for multi-segment levels. We find that this method reliably finds links between segments and is customizable to meet a designer's needs.

Related papers

• GlueStick: Robust Image Matching by Sticking Points and Lines Together [64.18659491529382]
  This paper introduces a new matching paradigm, where points, lines and descriptors are unified into a single wireframe structure. We show that our strategy outperforms that of other matching approaches independently.
  arXiv  Detail & Related papers  (2023-04-04T17:58:14Z)
• SceneHGN: Hierarchical Graph Networks for 3D Indoor Scene Generation with Fine-Grained Geometry [92.24144643757963]
  3D indoor scenes are widely used in computer graphics, with applications ranging from interior design to gaming to virtual and augmented reality. High-quality 3D indoor scenes are highly demanded while it requires expertise and is time-consuming to design high-quality 3D indoor scenes manually. We propose SCENEHGN, a hierarchical graph network for 3D indoor scenes that takes into account the full hierarchy from the room level to the object level, then finally to the object part level. For the first time, our method is able to directly generate plausible 3D room content, including furniture objects with fine-grained geometry, and
  arXiv  Detail & Related papers  (2023-02-16T15:31:59Z)
• Improving Semantic Segmentation in Transformers using Hierarchical Inter-Level Attention [68.7861229363712]
  Hierarchical Inter-Level Attention (HILA) is an attention-based method that captures Bottom-Up and Top-Down Updates between features of different levels. HILA extends hierarchical vision transformer architectures by adding local connections between features of higher and lower levels to the backbone encoder. We show notable improvements in accuracy in semantic segmentation with fewer parameters and FLOPS.
  arXiv  Detail & Related papers  (2022-07-05T15:47:31Z)
• Mario Plays on a Manifold: Generating Functional Content in Latent Space through Differential Geometry [7.863826008567604]
  We propose a method for reliable and random walks in the latent spaces of Categorical VAEs. We test our method with "Super Mario Bros" and "The Legend of Zelda" levels. Results show that the geometry we propose is better able to interpolate and sample, reliably staying closer to parts of the latent space that decode to playable content.
  arXiv  Detail & Related papers  (2022-05-31T20:39:56Z)
• Dungeon and Platformer Level Blending and Generation using Conditional VAEs [7.919213739992465]
  conditional VAEs (CVAEs) were recently shown capable of generating output that can be modified using labels specifying desired content. We expand these works by using CVAEs for generating whole platformer and dungeon levels, and blending levels across these genres.
  arXiv  Detail & Related papers  (2021-06-17T05:46:03Z)
• Learning Fine-Grained Segmentation of 3D Shapes without Part Labels [29.837938445219528]
  Learning-based 3D shape segmentation is usually formulated as a semantic labeling problem, assuming that all parts of training shapes are annotated with a given set of tags. We propose deep clustering to learn part priors from a shape dataset with fine-grained segmentation but no part labels. Our method is evaluated with a challenging benchmark of fine-grained segmentation, showing state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-03-24T07:27:07Z)
• Using Multiple Generative Adversarial Networks to Build Better-Connected Levels for Mega Man [0.0]
  This paper focuses on combining GAN-generated segments in a snaking pattern to create levels for Mega Man. Multiple GANs were trained on different types of segments to ensure better flow between segments.
  arXiv  Detail & Related papers  (2021-01-30T23:34:15Z)
• VideoClick: Video Object Segmentation with a Single Click [93.7733828038616]
  We propose a bottom up approach where given a single click for each object in a video, we obtain the segmentation masks of these objects in the full video. In particular, we construct a correlation volume that assigns each pixel in a target frame to either one of the objects in the reference frame or the background. Results on this new CityscapesVideo dataset show that our approach outperforms all the baselines in this challenging setting.
  arXiv  Detail & Related papers  (2021-01-16T23:07:48Z)
• Boundary-Aware Segmentation Network for Mobile and Web Applications [60.815545591314915]
  Boundary-Aware Network (BASNet) is integrated with a predict-refine architecture and a hybrid loss for highly accurate image segmentation. BASNet runs at over 70 fps on a single GPU which benefits many potential real applications. Based on BASNet, we further developed two (close to) commercial applications: AR COPY & PASTE, in which BASNet is augmented reality for "COPY" and "PASTING" real-world objects, and OBJECT CUT, which is a web-based tool for automatic object background removal.
  arXiv  Detail & Related papers  (2021-01-12T19:20:26Z)
• SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation [88.22349093672975]
  We design a weakly supervised point cloud segmentation algorithm that only requires clicking on one point per instance to indicate its location for annotation. With over-segmentation for pre-processing, we extend these location annotations into segments as seg-level labels. We show that our seg-level supervised method (SegGroup) achieves comparable results with the fully annotated point-level supervised methods.
  arXiv  Detail & Related papers  (2020-12-18T13:23:34Z)
• Sequential Segment-based Level Generation and Blending using Variational Autoencoders [5.33024001730262]
  We train VAEs to learn a sequential model of segment generation such that generated segments logically follow from prior segments. We obtain a pipeline that enables the generation of arbitrarily long levels that progress in any of these four directions. In addition to generating more coherent levels of non-fixed length, this method also enables implicit blending of levels from separate games.
  arXiv  Detail & Related papers  (2020-07-17T04:11:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.