Creative Portraiture: Exploring Creative Adversarial Networks and Conditional Creative Adversarial Networks

• URL: http://arxiv.org/abs/2412.07091v1
• Date: Tue, 10 Dec 2024 01:18:26 GMT
• Title: Creative Portraiture: Exploring Creative Adversarial Networks and Conditional Creative Adversarial Networks
• Authors: Sebastian Hereu, Qianfei Hu,
• Abstract summary: Convolutional neural networks (CNNs) have been combined with generative adversarial networks (GANs) to create deep convolutional generative adversarial networks (DCGANs) DCGANs have been used for generating images and videos from creative domains such as fashion design and painting. We explore an extension of DCGANs, creative adversarial networks (CANs)
• Score: 0.0
• License:
• Abstract: Convolutional neural networks (CNNs) have been combined with generative adversarial networks (GANs) to create deep convolutional generative adversarial networks (DCGANs) with great success. DCGANs have been used for generating images and videos from creative domains such as fashion design and painting. A common critique of the use of DCGANs in creative applications is that they are limited in their ability to generate creative products because the generator simply learns to copy the training distribution. We explore an extension of DCGANs, creative adversarial networks (CANs). Using CANs, we generate novel, creative portraits, using the WikiArt dataset to train the network. Moreover, we introduce our extension of CANs, conditional creative adversarial networks (CCANs), and demonstrate their potential to generate creative portraits conditioned on a style label. We argue that generating products that are conditioned, or inspired, on a style label closely emulates real creative processes in which humans produce imaginative work that is still rooted in previous styles.

Related papers

• Automating Creativity [1.0200170217746136]
  This paper explores what is required to evolve AI from generative to creative. We develop a triple prompt-response-reward engineering framework to develop the creative capability of GenAI.
  arXiv  Detail & Related papers  (2024-05-11T05:05:10Z)
• CreativeSynth: Creative Blending and Synthesis of Visual Arts based on Multimodal Diffusion [74.44273919041912]
  Large-scale text-to-image generative models have made impressive strides, showcasing their ability to synthesize a vast array of high-quality images. However, adapting these models for artistic image editing presents two significant challenges. We build the innovative unified framework Creative Synth, which is based on a diffusion model with the ability to coordinate multimodal inputs.
  arXiv  Detail & Related papers  (2024-01-25T10:42:09Z)
• A New Creative Generation Pipeline for Click-Through Rate with Stable Diffusion Model [8.945197427679924]
  Traditional AI-based approaches face the same problem of not considering user information while having limited aesthetic knowledge from designers. To optimize the results, the generated creatives in traditional methods are then ranked by another module named creative ranking model. This paper proposes a new automated Creative Generation pipeline for Click-Through Rate (CG4CTR) with the goal of improving CTR during the creative generation stage.
  arXiv  Detail & Related papers  (2024-01-17T03:27:39Z)
• ConceptLab: Creative Concept Generation using VLM-Guided Diffusion Prior Constraints [56.824187892204314]
  We present the task of creative text-to-image generation, where we seek to generate new members of a broad category. We show that the creative generation problem can be formulated as an optimization process over the output space of the diffusion prior. We incorporate a question-answering Vision-Language Model (VLM) that adaptively adds new constraints to the optimization problem, encouraging the model to discover increasingly more unique creations.
  arXiv  Detail & Related papers  (2023-08-03T17:04:41Z)
• Distilling Representations from GAN Generator via Squeeze and Span [55.76208869775715]
  We propose to distill knowledge from GAN generators by squeezing and spanning their representations. We span the distilled representation of the synthetic domain to the real domain by also using real training data to remedy the mode collapse of GANs.
  arXiv  Detail & Related papers  (2022-11-06T01:10:28Z)
• DC-Art-GAN: Stable Procedural Content Generation using DC-GANs for Digital Art [4.9631159466100305]
  We advocate the concept of using deep generative networks with adversarial training for a stable and variant art generation. The work mainly focuses on using the Deep Convolutional Generative Adversarial Network (DC-GAN) and explores the techniques to address the common pitfalls in GAN training.
  arXiv  Detail & Related papers  (2022-09-06T23:06:46Z)
• Towards Creativity Characterization of Generative Models via Group-based Subset Scanning [64.6217849133164]
  We propose group-based subset scanning to identify, quantify, and characterize creative processes. We find that creative samples generate larger subsets of anomalies than normal or non-creative samples across datasets.
  arXiv  Detail & Related papers  (2022-03-01T15:07:14Z)
• Exploring Latent Dimensions of Crowd-sourced Creativity [0.02294014185517203]
  We build our work on the largest AI-based creativity platform, Artbreeder. We explore the latent dimensions of images generated on this platform and present a novel framework for manipulating images to make them more creative.
  arXiv  Detail & Related papers  (2021-12-13T19:24:52Z)
• Towards creativity characterization of generative models via group-based subset scanning [51.84144826134919]
  We propose group-based subset scanning to quantify, detect, and characterize creative processes. Creative samples generate larger subsets of anomalies than normal or non-creative samples across datasets.
  arXiv  Detail & Related papers  (2021-04-01T14:07:49Z)
• Creative Sketch Generation [48.16835161875747]
  We introduce two datasets of creative sketches -- Creative Birds and Creative Creatures -- containing 10k sketches each along with part annotations. We propose DoodlerGAN -- a part-based Generative Adrial Network (GAN) -- to generate unseen compositions of novel part appearances. Quantitative evaluations as well as human studies demonstrate that sketches generated by our approach are more creative and of higher quality than existing approaches.
  arXiv  Detail & Related papers  (2020-11-19T18:57:00Z)

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.