Neural Texture Extraction and Distribution for Controllable Person Image Synthesis
We deal with the controllable person image synthesis task which aims to re-render a human from a reference image with explicit control over body pose and appearance. Observing that person images are highly structured, we propose to generate desired images by extracting and distributing semantic entities of reference images. To achieve this goal, a neural texture extraction and distribution operation based on double attention is described. This operation first extracts semantic neural textures from reference feature maps. Then, it distributes the extracted neural textures according to the spatial distributions learned from target poses. Our model is trained to predict human images in arbitrary poses, which encourages it to extract disentangled and expressive neural textures representing the appearance of different semantic entities. The disentangled representation further enables explicit appearance control. Neural textures of different reference images can be fused to control the appearance of the interested areas. Experimental comparisons show the superiority of the proposed model. Code is available at https://github.com/RenYurui/Neural-Texture-Extraction-Distribution.
Code (1)
Tasks
Image GenerationSimilar Papers 제목 키워드 기반
UVMap-ID: A Controllable and Personalized UV Map Generative Model
Recently, diffusion models have made significant strides in synthesizing realistic 2D human images based on provided text prompts. Building upon this, researchers have extended 2D text-to-image diffusion models into the …
AttributeControllable Person Image Synthesis with Spatially-Adaptive Warped Normalization
Controllable person image generation aims to produce realistic human images with desirable attributes such as a given pose, cloth textures, or hairstyles. However, the large spatial misalignment between source and target…
Image GenerationImage-to-Image TranslationPose TransferTranslationCorrespondence Learning for Controllable Person Image Generation
We present a generative model for controllable person image synthesis,as shown in Figure , which can be applied to pose-guided person image synthesis, $i.e.$, converting the pose of a source person image to the target po…
AttributeImage GenerationCross Attention Based Style Distribution for Controllable Person Image Synthesis
Controllable person image synthesis task enables a wide range of applications through explicit control over body pose and appearance. In this paper, we propose a cross attention based style distribution module that compu…
Image GenerationPose TransferVirtual Try-onDisentangled Representation Learning for Controllable Person Image Generation
In this paper, we propose a novel framework named DRL-CPG to learn disentangled latent representation for controllable person image generation, which can produce realistic person images with desired poses and human attri…
AttributeDecoderImage GenerationRepresentation Learning