Sequential Segment-based Level Generation and Blending using Variational Autoencoders

• URL: http://arxiv.org/abs/2007.08746v1
• Date: Fri, 17 Jul 2020 04:11:51 GMT
• Title: Sequential Segment-based Level Generation and Blending using Variational Autoencoders
• Authors: Anurag Sarkar, Seth Cooper
• Abstract summary: 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.
• Score: 5.33024001730262
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing methods of level generation using latent variable models such as VAEs and GANs do so in segments and produce the final level by stitching these separately generated segments together. In this paper, we build on these methods by training VAEs to learn a sequential model of segment generation such that generated segments logically follow from prior segments. By further combining the VAE with a classifier that determines whether to place the generated segment to the top, bottom, left or right of the previous segment, we obtain a pipeline that enables the generation of arbitrarily long levels that progress in any of these four directions and are composed of segments that logically follow one another. In addition to generating more coherent levels of non-fixed length, this method also enables implicit blending of levels from separate games that do not have similar orientation. We demonstrate our approach using levels from Super Mario Bros., Kid Icarus and Mega Man, showing that our method produces levels that are more coherent than previous latent variable-based approaches and are capable of blending levels across games.

Related papers

• SFGANS Self-supervised Future Generator for human ActioN Segmentation [2.3020018305241337]
  We present a self-supervised method that comes in the middle of the standard pipeline and generates refined representations of the original feature vectors. Experiments show that this method improves the performance of existing models on different sub-tasks of action segmentation, even without additional hyper parameter tuning.
  arXiv  Detail & Related papers  (2023-12-31T09:36:55Z)
• SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion Process [102.18226145874007]
  We propose a model-agnostic solution called SegRefiner to enhance the quality of object masks produced by different segmentation models. SegRefiner takes coarse masks as inputs and refines them using a discrete diffusion process. It consistently improves both the segmentation metrics and boundary metrics across different types of coarse masks.
  arXiv  Detail & Related papers  (2023-12-19T18:53:47Z)
• SemAffiNet: Semantic-Affine Transformation for Point Cloud Segmentation [94.11915008006483]
  We propose SemAffiNet for point cloud semantic segmentation. We conduct extensive experiments on the ScanNetV2 and NYUv2 datasets.
  arXiv  Detail & Related papers  (2022-05-26T17:00:23Z)
• Scaling up Multi-domain Semantic Segmentation with Sentence Embeddings [81.09026586111811]
  We propose an approach to semantic segmentation that achieves state-of-the-art supervised performance when applied in a zero-shot setting. This is achieved by replacing each class label with a vector-valued embedding of a short paragraph that describes the class. The resulting merged semantic segmentation dataset of over 2 Million images enables training a model that achieves performance equal to that of state-of-the-art supervised methods on 7 benchmark datasets.
  arXiv  Detail & Related papers  (2022-02-04T07:19:09Z)
• Ensemble Learning For Mega Man Level Generation [2.6402344419230697]
  We investigate the use of ensembles of Markov chains for procedurally generating emphMega Man levels. We evaluate it on measures of playability and stylistic similarity in comparison to a non-ensemble, existing Markov chain approach.
  arXiv  Detail & Related papers  (2021-07-27T00:16:23Z)
• Hybrid Encoding For Generating Large Scale Game Level Patterns With Local Variations Using a GAN [5.144809478361604]
  We propose a new hybrid approach that evolves CPPNs first, but allows the latent vectors to evolve later, and combines the benefits of both approaches. These approaches are evaluated in Super Mario Bros. and The Legend of Zelda.
  arXiv  Detail & Related papers  (2021-05-27T06:27:19Z)
• Segmenter: Transformer for Semantic Segmentation [79.9887988699159]
  We introduce Segmenter, a transformer model for semantic segmentation. We build on the recent Vision Transformer (ViT) and extend it to semantic segmentation. It outperforms the state of the art on the challenging ADE20K dataset and performs on-par on Pascal Context and Cityscapes.
  arXiv  Detail & Related papers  (2021-05-12T13:01:44Z)
• 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)
• 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)
• TOAD-GAN: Coherent Style Level Generation from a Single Example [24.039037782220017]
  We present TOAD-GAN, a novel Procedural Content Generation (PCG) algorithm that generates token-based video game levels. We demonstrate its application for Super Mario Bros. levels and are able to generate new levels of similar style in arbitrary sizes.
  arXiv  Detail & Related papers  (2020-08-04T13:44:50Z)
• Self-Supervised Tuning for Few-Shot Segmentation [82.32143982269892]
  Few-shot segmentation aims at assigning a category label to each image pixel with few annotated samples. Existing meta-learning method tends to fail in generating category-specifically discriminative descriptor when the visual features extracted from support images are marginalized in embedding space. This paper presents an adaptive framework tuning, in which the distribution of latent features across different episodes is dynamically adjusted based on a self-segmentation scheme.
  arXiv  Detail & Related papers  (2020-04-12T03:53:53Z)

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.