Re:Draw -- Context Aware Translation as a Controllable Method for
Artistic Production
- URL: http://arxiv.org/abs/2401.03499v1
- Date: Sun, 7 Jan 2024 14:34:34 GMT
- Title: Re:Draw -- Context Aware Translation as a Controllable Method for
Artistic Production
- Authors: Joao Liborio Cardoso, Francesco Banterle, Paolo Cignoni, Michael
Wimmer
- Abstract summary: We introduce context-aware translation, a novel method that combines the benefits of inpainting and image-to-image translation.
As an use case, we apply our method to redraw any hand-drawn animated character eyes based on any design specifications.
- Score: 8.383295377277836
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: We introduce context-aware translation, a novel method that combines the
benefits of inpainting and image-to-image translation, respecting
simultaneously the original input and contextual relevance -- where existing
methods fall short. By doing so, our method opens new avenues for the
controllable use of AI within artistic creation, from animation to digital art.
As an use case, we apply our method to redraw any hand-drawn animated
character eyes based on any design specifications - eyes serve as a focal point
that captures viewer attention and conveys a range of emotions, however, the
labor-intensive nature of traditional animation often leads to compromises in
the complexity and consistency of eye design. Furthermore, we remove the need
for production data for training and introduce a new character recognition
method that surpasses existing work by not requiring fine-tuning to specific
productions. This proposed use case could help maintain consistency throughout
production and unlock bolder and more detailed design choices without the
production cost drawbacks. A user study shows context-aware translation is
preferred over existing work 95.16% of the time.
Related papers
- Text Guided Image Editing with Automatic Concept Locating and Forgetting [27.70615803908037]
We propose a novel method called Locate and Forget (LaF) to locate potential target concepts in the image for modification.
Compared to the baselines, our method demonstrates its superiority in text-guided image editing tasks both qualitatively and quantitatively.
arXiv Detail & Related papers (2024-05-30T05:36:32Z) - Dynamic Typography: Bringing Text to Life via Video Diffusion Prior [73.72522617586593]
We present an automated text animation scheme, termed "Dynamic Typography"
It deforms letters to convey semantic meaning and infuses them with vibrant movements based on user prompts.
Our technique harnesses vector graphics representations and an end-to-end optimization-based framework.
arXiv Detail & Related papers (2024-04-17T17:59:55Z) - HAIFIT: Human-to-AI Fashion Image Translation [6.034505799418777]
We introduce HAIFIT, a novel approach that transforms sketches into high-fidelity, lifelike clothing images.
Our method excels in preserving the distinctive style and intricate details essential for fashion design applications.
arXiv Detail & Related papers (2024-03-13T16:06:07Z) - Training-Free Consistent Text-to-Image Generation [80.4814768762066]
Text-to-image models can portray the same subject across diverse prompts.
Existing approaches fine-tune the model to teach it new words that describe specific user-provided subjects.
We present ConsiStory, a training-free approach that enables consistent subject generation by sharing the internal activations of the pretrained model.
arXiv Detail & Related papers (2024-02-05T18:42:34Z) - SketchDreamer: Interactive Text-Augmented Creative Sketch Ideation [111.2195741547517]
We present a method to generate controlled sketches using a text-conditioned diffusion model trained on pixel representations of images.
Our objective is to empower non-professional users to create sketches and, through a series of optimisation processes, transform a narrative into a storyboard.
arXiv Detail & Related papers (2023-08-27T19:44:44Z) - ImageBrush: Learning Visual In-Context Instructions for Exemplar-Based
Image Manipulation [49.07254928141495]
We propose a novel manipulation methodology, dubbed ImageBrush, that learns visual instructions for more accurate image editing.
Our key idea is to employ a pair of transformation images as visual instructions, which precisely captures human intention.
Our model exhibits robust generalization capabilities on various downstream tasks such as pose transfer, image translation and video inpainting.
arXiv Detail & Related papers (2023-08-02T01:57:11Z) - Text-Guided Synthesis of Eulerian Cinemagraphs [81.20353774053768]
We introduce Text2Cinemagraph, a fully automated method for creating cinemagraphs from text descriptions.
We focus on cinemagraphs of fluid elements, such as flowing rivers, and drifting clouds, which exhibit continuous motion and repetitive textures.
arXiv Detail & Related papers (2023-07-06T17:59:31Z) - Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors [58.71128866226768]
Recent text-to-image generation methods have incrementally improved the generated image fidelity and text relevancy.
We propose a novel text-to-image method that addresses these gaps by (i) enabling a simple control mechanism complementary to text in the form of a scene.
Our model achieves state-of-the-art FID and human evaluation results, unlocking the ability to generate high fidelity images in a resolution of 512x512 pixels.
arXiv Detail & Related papers (2022-03-24T15:44:50Z) - Generative Art Using Neural Visual Grammars and Dual Encoders [25.100664361601112]
A novel algorithm for producing generative art is described.
It allows a user to input a text string, and which in a creative response to this string, outputs an image.
arXiv Detail & Related papers (2021-05-01T04:21:52Z)
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.