A Progressive Approach to Content Generation

Abstract. PCG approaches are commonly categorised as constructive, generate-and-test or search-based. Each of these approaches has its distinctive advantages and drawbacks. In this paper, we propose an approach to Content Generation (CG) – in particular level generation – that combines the advantages of construc-tive and search-based approaches thus providing a fast, flexible and reliable way of generating diverse content of high quality. In our framework, CG is seen from a new perspective which differentiates between two main aspects of the game-play experience, namely the order of the in-game interactions and the associated level design. The framework first generates timelines following the search-based paradigm. Timelines are game-independent and they reflect the rhythmic feel of the levels. A progressive, constructive-based approach is then implemented to evaluate timelines by mapping them into level designs. The framework is applied for the generation of puzzles for the Cut the Rope game and the results in terms of performance, expressivity and controllability are characterised and discussed.

DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation

Recent advances in 3D content creation mostly leverage optimization-based 3D generation via score distillation sampling (SDS). Though promising results have been exhibited, these methods often suffer from slow per-sample optimization, limiting their practical usage. In this paper, we propose DreamGaussian, a novel 3D content generation framework that achieves both efficiency and quality simultaneously. Our key insight is to design a generative 3D Gaussian Splatting model with companioned mesh extraction and texture refinement in UV space. In contrast to the occupancy pruning used in Neural Radiance Fields, we demonstrate that the progressive densification of 3D Gaussians converges significantly faster for 3D generative tasks. To further enhance the texture quality and facilitate downstream applications, we introduce an efficient algorithm to convert 3D Gaussians into textured meshes and apply a fine-tuning stage to refine the details. Extensive experiments demonstrate the superior efficiency and competitive generation quality of our proposed approach. Notably, DreamGaussian produces high-quality textured meshes in just 2 minutes from a single-view image, achieving approximately 10 times acceleration compared to existing methods.Comment: project page: https://dreamgaussian.github.io

Progressive Content Generation Based on Cyclic Graph for Generate Dungeon

Dungeon is level in game consisting collection of rooms and doors with obstacles inside. To make good level, takes a lot of time. With Procedural Content Generation (PCG), dungeons can be created automatically. One of the approaches in PCG to create levels is progressive. Progressive approach produces timeline as representation of the interactions in the game. Timeline representation that is in the form of one straight line is good for endless runner, but for dungeon, the levels are linear. In this research, the timeline is changed to cyclic graph. Cyclic graph is formed using graph grammar algorithm. This research aims to build dungeon that has not linear and minimal dead ends. To eliminate linearity in dungeons, branching in dungeons needs to be formed. The steps carried out in this research are designing graph grammar rules, generating population of graphs, evaluating graphs with fitness values, and building dungeons. Four functions are used to determine the fitness value: shortest vertices, average duration, replayability, and variation. Dungeons produced with progressive approach manage to minimize linearity in dungeons. Dungeon formation is very dependent on the rule grammar that forms it. With the evaluation process, linear dungeons resulting from grammar rules can be minimized

Video Synthesis from the StyleGAN Latent Space

Generative models have shown impressive results in generating synthetic images. However, video synthesis is still difficult to achieve, even for these generative models. The best videos that generative models can currently create are a few seconds long, distorted, and low resolution. For this project, I propose and implement a model to synthesize videos at 1024x1024x32 resolution that include human facial expressions by using static images generated from a Generative Adversarial Network trained on the human facial images. To the best of my knowledge, this is the first work that generates realistic videos that are larger than 256x256 resolution from single starting images. This model improves the video synthesis in both quantitative and qualitative ways compared to two state-of-the-art models: TGAN and MocoGAN. In a quantitative comparison, this project reaches a best Average Content Distance (ACD) score of 0.167, as compared to 0.305 and 0.201 of TGAN and MocoGAN, respectively

Modeling Progressive Fibrosis with Pluripotent Stem Cells Identifies an Anti-fibrotic Small Molecule.

Progressive organ fibrosis accounts for one-third of all deaths worldwide, yet preclinical models that mimic the complex, progressive nature of the disease are lacking, and hence, there are no curative therapies. Progressive fibrosis across organs shares common cellular and molecular pathways involving chronic injury, inflammation, and aberrant repair resulting in deposition of extracellular matrix, organ remodeling, and ultimately organ failure. We describe the generation and characterization of an in vitro progressive fibrosis model that uses cell types derived from induced pluripotent stem cells. Our model produces endogenous activated transforming growth factor β (TGF-β) and contains activated fibroblastic aggregates that progressively increase in size and stiffness with activation of known fibrotic molecular and cellular changes. We used this model as a phenotypic drug discovery platform for modulators of fibrosis. We validated this platform by identifying a compound that promotes resolution of fibrosis in in vivo and ex vivo models of ocular and lung fibrosis

Building consensus on Internet access at the Internet Governance Forum (IGF)

This paper identifies and documents the main areas of discussions and 'recommendations' that were generated under the Access theme at the second Internet Governance Forum in Rio De Janeiro, November 2007. Whilst recognising that the IGF is currently viewed and operates primarily as a space for discussion, the paper finds that (specifically in the case of Access) it is also a space in which commonality of opinion occurs to the level at which 'recommendations' can be made and repeatedly asserted independently/individually in the workshops, and strategically reinforced at different levels of the IGF. The Internet Governance Forum (IGF) is a space for multi-stakeholder policy dialogue, set up in 2006 as a direct response to the deliberations of the World Summit on the Information Society (WSIS). The forum was created to (amongst other things) discuss public policy issues related to key elements of internet governance in order to foster the sustainability, robustness, security, stability and development of the internet. Its structure, function and working are addressed in paragraphs 73 to 79 of the WSIS Tunis Agenda