ReshapeGAN: Object Reshaping by Providing A Single Reference Image

2019-05-16 · Ziqiang Zheng, Yang Wu, Zhibin Yu, Yang Yang, Haiyong Zheng, Takeo Kanade

The aim of this work is learning to reshape the object in an input image to an arbitrary new shape, by just simply providing a single reference image with an object instance in the desired shape. We propose a new Generative Adversarial Network (GAN) architecture for such an object reshaping problem, named ReshapeGAN. The network can be tailored for handling all kinds of problem settings, including both within-domain (or single-dataset) reshaping and cross-domain (typically across mutiple datasets) reshaping, with paired or unpaired training data. The appearance of the input object is preserved in all cases, and thus it is still identifiable after reshaping, which has never been achieved as far as we are aware. We present the tailored models of the proposed ReshapeGAN for all the problem settings, and have them tested on 8 kinds of reshaping tasks with 13 different datasets, demonstrating the ability of ReshapeGAN on generating convincing and superior results for object reshaping. To the best of our knowledge, we are the first to be able to make one GAN framework work on all such object reshaping tasks, especially the cross-domain tasks on handling multiple diverse datasets. We present here both ablation studies on our proposed ReshapeGAN models and comparisons with the state-of-the-art models when they are made comparable, using all kinds of applicable metrics that we are aware of.

📄 PDF Abstract BibTeX arXiv:1905.06514

Code (1)

zhengziqiang/ReshapeGAN 공식 구현 tf

Tasks

AllGenerative Adversarial NetworkObject

Methods 이 논문이 사용한 방법론

Convolution A convolution is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the…

Dogecoin Customer Service Number +1-833-534-1729 설명 없음

Large Language Model Post-Training: A Unified View of Off-Policy and On-Policy Learning

2026-04-09 · Shiwan Zhao, Zhihu Wang, Xuyang Zhao, Jiaming Zhou 외 arxiv

Post-training has become central to turning pretrained large language models (LLMs) into aligned, capable, and deployable systems. Recent progress spans supervised fine-tuning (SFT), preference optimization, reinforcemen…

Reinforcement Learning

Graph Neural Network approaches for single-cell data: A recent overview

2023-10-14 · Konstantinos Lazaros, Dimitris E. Koumadorakis, Panagiotis Vlamos, Aristidis G. Vrahatis

Graph Neural Networks (GNN) are reshaping our understanding of biomedicine and diseases by revealing the deep connections among genes and cells. As both algorithmic and biomedical technologies have advanced significantly…

Data Integration Diversity Graph Attention Graph Neural Network+1

Verifier-Induced Support Reshaping in On-Policy Optimization

2026-07-31 · Shaohang Wei, Zikun Su, Feifan Song, Wen Luo 외 hf

We show that on-policy reinforcement learning with verifiable rewards (RLVR) can improve the current objective while making successful behaviors for later objectives too rare to sample and reinforce. We call this verifie…

Mathematical Reasoning Reinforcement Learning Instruction Following

Active Preference Learning over Latent Preference Archetypes for Many-Objective Bayesian Optimization

2026-03-30 · Manisha Dubey, Sebastiaan De Peuter, Wanrong Wang, Samuel Kaski arxiv

Preference-based many-objective Bayesian optimization typically assumes that all pairwise comparisons arise from a single latent utility function, despite real decision makers often exhibiting multiple latent preference …

ParetoSlider: Diffusion Models Post-Training for Continuous Reward Control

2026-04-22 · Shelly Golan, Michael Finkelson, Ariel Bereslavsky, Yotam Nitzan 외 arxiv

Reinforcement Learning (RL) post-training has become the standard for aligning generative models with human preferences, yet most methods rely on a single scalar reward. When multiple criteria matter, the prevailing prac…

Reinforcement Learning Image Editing