Papers Appearance Transfer
“Appearance Transfer” 태그가 달린 논문 32편 · 필터 해제
HandOcc: NeRF-based Hand Rendering with Occupancy Networks
We propose HandOcc, a novel framework for hand rendering based upon occupancy. Popular rendering methods such as NeRF are often combined with parametric meshes to provide deformable hand models. However, in doing so, suc…
Appearance TransferNeRFSemantix: An Energy Guided Sampler for Semantic Style Transfer
Recent advances in style and appearance transfer are impressive, but most methods isolate global style and local appearance transfer, neglecting semantic correspondence. Additionally, image and video tasks are typically …
Appearance TransferSemantic correspondenceStyle TransferVisual Persona: Foundation Model for Full-Body Human Customization
We introduce Visual Persona, a foundation model for text-to-image full-body human customization that, given a single in-the-wild human image, generates diverse images of the individual guided by text descriptions. Unlike…
Appearance TransferReStyle3D: Scene-Level Appearance Transfer with Semantic Correspondences
We introduce ReStyle3D, a novel framework for scene-level appearance transfer from a single style image to a real-world scene represented by multiple views. The method combines explicit semantic correspondences with mult…
Appearance TransferLineArt: A Knowledge-guided Training-free High-quality Appearance Transfer for Design Drawing with Diffusion Model
Image rendering from line drawings is vital in design and image generation technologies reduce costs, yet professional line drawings demand preserving complex details. Text prompts struggle with accuracy, and image trans…
Appearance TransferImage GenerationPose-Based Sign Language Appearance Transfer
We introduce a method for transferring the signer's appearance in sign language skeletal poses while preserving the sign content. Using estimated poses, we transfer the appearance of one signer to another, maintaining na…
Appearance TransferMask-guided cross-image attention for zero-shot in-silico histopathologic image generation with a diffusion model
Creating in-silico data with generative AI promises a cost-effective alternative to staining, imaging, and annotating whole slide images in computational pathology. Diffusion models are the state-of-the-art solution for …
Appearance TransferImage Generationwhole slide imagesEye-for-an-eye: Appearance Transfer with Semantic Correspondence in Diffusion Models
As pretrained text-to-image diffusion models have become a useful tool for image synthesis, people want to specify the results in various ways. In this paper, we introduce a method to produce results with the same struct…
Appearance TransferImage GenerationSemantic correspondenceCtrl-X: Controlling Structure and Appearance for Text-To-Image Generation Without Guidance
Recent controllable generation approaches such as FreeControl and Diffusion Self-Guidance bring fine-grained spatial and appearance control to text-to-image (T2I) diffusion models without training auxiliary modules. Howe…
Appearance TransferImage GenerationText to Image GenerationText-to-Image GenerationFilterPrompt: A Simple yet Efficient Approach to Guide Image Appearance Transfer in Diffusion Models
In controllable generation tasks, flexibly manipulating the generated images to attain a desired appearance or structure based on a single input image cue remains a critical and longstanding challenge. Achieving this req…
Appearance TransferFeature CorrelationZeST: Zero-Shot Material Transfer from a Single Image
We propose ZeST, a method for zero-shot material transfer to an object in the input image given a material exemplar image. ZeST leverages existing diffusion adapters to extract implicit material representation from the e…
Appearance TransferObjectNeRF Analogies: Example-Based Visual Attribute Transfer for NeRFs
A Neural Radiance Field (NeRF) encodes the specific relation of 3D geometry and appearance of a scene. We here ask the question whether we can transfer the appearance from a source NeRF onto a target 3D geometry in a sem…
3D geometryAppearance TransferAttributeNeRFUnified Diffusion-Based Rigid and Non-Rigid Editing with Text and Image Guidance
Existing text-to-image editing methods tend to excel either in rigid or non-rigid editing but encounter challenges when combining both, resulting in misaligned outputs with the provided text prompts. In addition, integra…
Appearance TransferFine-grained Appearance Transfer with Diffusion Models
Image-to-image translation (I2I), and particularly its subfield of appearance transfer, which seeks to alter the visual appearance between images while maintaining structural coherence, presents formidable challenges. De…
Appearance TransferImage-to-Image TranslationDisentangling Structure and Appearance in ViT Feature Space
We present a method for semantically transferring the visual appearance of one natural image to another. Specifically, our goal is to generate an image in which objects in a source structure image are "painted" with the …
Appearance TransferSemantic SegmentationCross-Image Attention for Zero-Shot Appearance Transfer
Recent advancements in text-to-image generative models have demonstrated a remarkable ability to capture a deep semantic understanding of images. In this work, we leverage this semantic knowledge to transfer the visual a…
Appearance TransferDenoisingSeeing is not Believing: An Identity Hider for Human Vision Privacy Protection
Massive captured face images are stored in the database for the identification of individuals. However, these images can be observed unintentionally by data managers, which is not at the will of individuals and may cause…
Appearance TransferAttributeDisentanglementFace GenerationRepresentation Learning for Visual Object Tracking by Masked Appearance Transfer
Visual representation plays an important role in visual object tracking. However, few works study the tracking-specified representation learning method. Most trackers directly use ImageNet pre-trained representations…
Appearance TransferDecoderObject TrackingRepresentation Learning+1Splicing ViT Features for Semantic Appearance Transfer
We present a method for semantically transferring the visual appearance of one natural image to another. Specifically, our goal is to generate an image in which objects in a source structure image are "painted" with the …
Appearance TransferImage GenerationStyle TransferSuper-Resolution Appearance Transfer for 4D Human Performances
A common problem in the 4D reconstruction of people from multi-view video is the quality of the captured dynamic texture appearance which depends on both the camera resolution and capture volume. Typically the requiremen…
4D reconstruction4k8kAppearance Transfer+1