TPIE: Topology-Preserved Image Editing With Text Instructions

• URL: http://arxiv.org/abs/2411.16714v1
• Date: Fri, 22 Nov 2024 22:08:27 GMT
• Title: TPIE: Topology-Preserved Image Editing With Text Instructions
• Authors: Nivetha Jayakumar, Srivardhan Reddy Gadila, Tonmoy Hossain, Yangfeng Ji, Miaomiao Zhang,
• Abstract summary: Topology-Preserved Image Editing with text instructions (TPIE) TPIE treats newly generated samples as deformable variations of a given input template, allowing for controllable and structure-preserving edits. We validate TPIE on a diverse set of 2D and 3D images and compare them with state-of-the-art image editing approaches.
• Score: 14.399084325078878
• License:
• Abstract: Preserving topological structures is important in real-world applications, particularly in sensitive domains such as healthcare and medicine, where the correctness of human anatomy is critical. However, most existing image editing models focus on manipulating intensity and texture features, often overlooking object geometry within images. To address this issue, this paper introduces a novel method, Topology-Preserved Image Editing with text instructions (TPIE), that for the first time ensures the topology and geometry remaining intact in edited images through text-guided generative diffusion models. More specifically, our method treats newly generated samples as deformable variations of a given input template, allowing for controllable and structure-preserving edits. Our proposed TPIE framework consists of two key modules: (i) an autoencoder-based registration network that learns latent representations of object transformations, parameterized by velocity fields, from pairwise training images; and (ii) a novel latent conditional geometric diffusion (LCDG) model efficiently capturing the data distribution of learned transformation features conditioned on custom-defined text instructions. We validate TPIE on a diverse set of 2D and 3D images and compare them with state-of-the-art image editing approaches. Experimental results show that our method outperforms other baselines in generating more realistic images with well-preserved topology. Our code will be made publicly available on Github.

Related papers

• A Survey of Multimodal-Guided Image Editing with Text-to-Image Diffusion Models [117.77807994397784]
  Image editing aims to edit the given synthetic or real image to meet the specific requirements from users. Recent significant advancement in this field is based on the development of text-to-image (T2I) diffusion models. T2I-based image editing methods significantly enhance editing performance and offer a user-friendly interface for modifying content guided by multimodal inputs.
  arXiv  Detail & Related papers  (2024-06-20T17:58:52Z)
• Diffusion Models are Geometry Critics: Single Image 3D Editing Using Pre-Trained Diffusion Priors [24.478875248825563]
  We propose a novel image editing technique that enables 3D manipulations on single images. Our method directly leverages powerful image diffusion models trained on a broad spectrum of text-image pairs. Our method can generate high-quality 3D-aware image edits with large viewpoint transformations and high appearance and shape consistency with the input image.
  arXiv  Detail & Related papers  (2024-03-18T06:18:59Z)
• GeoDiffusion: Text-Prompted Geometric Control for Object Detection Data Generation [91.01581867841894]
  We propose the GeoDiffusion, a simple framework that can flexibly translate various geometric conditions into text prompts. Our GeoDiffusion is able to encode not only the bounding boxes but also extra geometric conditions such as camera views in self-driving scenes.
  arXiv  Detail & Related papers  (2023-06-07T17:17:58Z)
• iEdit: Localised Text-guided Image Editing with Weak Supervision [53.082196061014734]
  We propose a novel learning method for text-guided image editing. It generates images conditioned on a source image and a textual edit prompt. It shows favourable results against its counterparts in terms of image fidelity, CLIP alignment score and qualitatively for editing both generated and real images.
  arXiv  Detail & Related papers  (2023-05-10T07:39:14Z)
• Delta-GAN-Encoder: Encoding Semantic Changes for Explicit Image Editing, using Few Synthetic Samples [2.348633570886661]
  We propose a novel method for learning to control any desired attribute in a pre-trained GAN's latent space. We perform Sim2Real learning, relying on minimal samples to achieve an unlimited amount of continuous precise edits.
  arXiv  Detail & Related papers  (2021-11-16T12:42:04Z)
• Controllable Person Image Synthesis with Spatially-Adaptive Warped Normalization [72.65828901909708]
  Controllable person image generation aims to produce realistic human images with desirable attributes. We introduce a novel Spatially-Adaptive Warped Normalization (SAWN), which integrates a learned flow-field to warp modulation parameters. We propose a novel self-training part replacement strategy to refine the pretrained model for the texture-transfer task.
  arXiv  Detail & Related papers  (2021-05-31T07:07:44Z)
• Text to Image Generation with Semantic-Spatial Aware GAN [41.73685713621705]
  A text to image generation (T2I) model aims to generate photo-realistic images which are semantically consistent with the text descriptions. We propose a novel framework Semantic-Spatial Aware GAN, which is trained in an end-to-end fashion so that the text encoder can exploit better text information.
  arXiv  Detail & Related papers  (2021-04-01T15:48:01Z)
• PerceptionGAN: Real-world Image Construction from Provided Text through Perceptual Understanding [11.985768957782641]
  We propose a method to provide good images by incorporating perceptual understanding in the discriminator module. We show that the perceptual information included in the initial image is improved while modeling image distribution at multiple stages. More importantly, the proposed method can be integrated into the pipeline of other state-of-the-art text-based-image-generation models.
  arXiv  Detail & Related papers  (2020-07-02T09:23:08Z)
• Structural-analogy from a Single Image Pair [118.61885732829117]
  In this paper, we explore the capabilities of neural networks to understand image structure given only a single pair of images, A and B. We generate an image that keeps the appearance and style of B, but has a structural arrangement that corresponds to A. Our method can be used to generate high quality imagery in other conditional generation tasks utilizing images A and B only.
  arXiv  Detail & Related papers  (2020-04-05T14:51:10Z)
• Two-shot Spatially-varying BRDF and Shape Estimation [89.29020624201708]
  We propose a novel deep learning architecture with a stage-wise estimation of shape and SVBRDF. We create a large-scale synthetic training dataset with domain-randomized geometry and realistic materials. Experiments on both synthetic and real-world datasets show that our network trained on a synthetic dataset can generalize well to real-world images.
  arXiv  Detail & Related papers  (2020-04-01T12:56:13Z)

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.