Structure-Aware Flow Generation for Human Body Reshaping
- URL: http://arxiv.org/abs/2203.04670v2
- Date: Fri, 11 Mar 2022 03:38:21 GMT
- Title: Structure-Aware Flow Generation for Human Body Reshaping
- Authors: Jianqiang Ren, Yuan Yao, Biwen Lei, Miaomiao Cui, Xuansong Xie
- Abstract summary: We develop an end-to-end flow generation architecture to achieve unprecedentedly controllable performance under arbitrary poses and garments.
For a comprehensive evaluation, we construct the first large-scale body reshaping dataset, namely BR-5K.
Our approach significantly outperforms existing state-of-the-art methods in terms of visual performance, controllability, and efficiency.
- Score: 15.365236395118982
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Body reshaping is an important procedure in portrait photo retouching. Due to
the complicated structure and multifarious appearance of human bodies, existing
methods either fall back on the 3D domain via body morphable model or resort to
keypoint-based image deformation, leading to inefficiency and unsatisfied
visual quality. In this paper, we address these limitations by formulating an
end-to-end flow generation architecture under the guidance of body structural
priors, including skeletons and Part Affinity Fields, and achieve
unprecedentedly controllable performance under arbitrary poses and garments. A
compositional attention mechanism is introduced for capturing both visual
perceptual correlations and structural associations of the human body to
reinforce the manipulation consistency among related parts. For a comprehensive
evaluation, we construct the first large-scale body reshaping dataset, namely
BR-5K, which contains 5,000 portrait photos as well as professionally retouched
targets. Extensive experiments demonstrate that our approach significantly
outperforms existing state-of-the-art methods in terms of visual performance,
controllability, and efficiency. The dataset is available at our website:
https://github.com/JianqiangRen/FlowBasedBodyReshaping.
Related papers
- 3D WholeBody Pose Estimation based on Semantic Graph Attention Network and Distance Information [2.457872341625575]
A novel Semantic Graph Attention Network can benefit from the ability of self-attention to capture global context.
A Body Part Decoder assists in extracting and refining the information related to specific segments of the body.
A Geometry Loss makes a critical constraint on the structural skeleton of the body, ensuring that the model's predictions adhere to the natural limits of human posture.
arXiv Detail & Related papers (2024-06-03T10:59:00Z) - Structure-Aware Human Body Reshaping with Adaptive Affinity-Graph Network [14.361677329761672]
We propose a novel Adaptive Affinity-Graph Network (AAGN), which extracts the global affinity between different body parts.
For high-frequency details, a Body Shape Discriminator (BSD) is designed to extract information from both high-frequency and spatial domain.
Our framework significantly enhances the aesthetic appeal of photos, marginally surpassing all previous work to achieve state-of-the-art in all evaluation metrics.
arXiv Detail & Related papers (2024-04-22T08:44:10Z) - DiffBody: Diffusion-based Pose and Shape Editing of Human Images [1.7188280334580193]
We propose a one-shot approach that enables large edits with identity preservation.
To enable large edits, we fit a 3D body model, project the input image onto the 3D model, and change the body's pose and shape.
We further enhance the realism by fine-tuning text embeddings via self-supervised learning.
arXiv Detail & Related papers (2024-01-05T13:36:19Z) - Towards Robust and Expressive Whole-body Human Pose and Shape Estimation [51.457517178632756]
Whole-body pose and shape estimation aims to jointly predict different behaviors of the entire human body from a monocular image.
Existing methods often exhibit degraded performance under the complexity of in-the-wild scenarios.
We propose a novel framework to enhance the robustness of whole-body pose and shape estimation.
arXiv Detail & Related papers (2023-12-14T08:17:42Z) - Human as Points: Explicit Point-based 3D Human Reconstruction from
Single-view RGB Images [78.56114271538061]
We introduce an explicit point-based human reconstruction framework called HaP.
Our approach is featured by fully-explicit point cloud estimation, manipulation, generation, and refinement in the 3D geometric space.
Our results may indicate a paradigm rollback to the fully-explicit and geometry-centric algorithm design.
arXiv Detail & Related papers (2023-11-06T05:52:29Z) - Pose Guided Human Image Synthesis with Partially Decoupled GAN [25.800174118151638]
Pose Guided Human Image Synthesis (PGHIS) is a challenging task of transforming a human image from the reference pose to a target pose.
We propose a method by decoupling the human body into several parts to guide the synthesis of a realistic image of the person.
In addition, we design a multi-head attention-based module for PGHIS.
arXiv Detail & Related papers (2022-10-07T15:31:37Z) - Generalizable Neural Performer: Learning Robust Radiance Fields for
Human Novel View Synthesis [52.720314035084215]
This work targets at using a general deep learning framework to synthesize free-viewpoint images of arbitrary human performers.
We present a simple yet powerful framework, named Generalizable Neural Performer (GNR), that learns a generalizable and robust neural body representation.
Experiments on GeneBody-1.0 and ZJU-Mocap show better robustness of our methods than recent state-of-the-art generalizable methods.
arXiv Detail & Related papers (2022-04-25T17:14:22Z) - LatentHuman: Shape-and-Pose Disentangled Latent Representation for Human
Bodies [78.17425779503047]
We propose a novel neural implicit representation for the human body.
It is fully differentiable and optimizable with disentangled shape and pose latent spaces.
Our model can be trained and fine-tuned directly on non-watertight raw data with well-designed losses.
arXiv Detail & Related papers (2021-11-30T04:10:57Z) - Structure-aware Person Image Generation with Pose Decomposition and
Semantic Correlation [29.727033198797518]
We propose a structure-aware flow based method for high-quality person image generation.
We decompose the human body into different semantic parts and apply different networks to predict the flow fields for these parts separately.
Our method can generate high-quality results under large pose discrepancy and outperforms state-of-the-art methods in both qualitative and quantitative comparisons.
arXiv Detail & Related papers (2021-02-05T03:07:57Z) - Perspective Plane Program Induction from a Single Image [85.28956922100305]
We study the inverse graphics problem of inferring a holistic representation for natural images.
We formulate this problem as jointly finding the camera pose and scene structure that best describe the input image.
Our proposed framework, Perspective Plane Program Induction (P3I), combines search-based and gradient-based algorithms to efficiently solve the problem.
arXiv Detail & Related papers (2020-06-25T21:18:58Z) - Kinematic-Structure-Preserved Representation for Unsupervised 3D Human
Pose Estimation [58.72192168935338]
Generalizability of human pose estimation models developed using supervision on large-scale in-studio datasets remains questionable.
We propose a novel kinematic-structure-preserved unsupervised 3D pose estimation framework, which is not restrained by any paired or unpaired weak supervisions.
Our proposed model employs three consecutive differentiable transformations named as forward-kinematics, camera-projection and spatial-map transformation.
arXiv Detail & Related papers (2020-06-24T23:56:33Z)
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.