The effect of skip connection for Faceshifer [35]. µs,t and σs,t: the mean and std. See more researchers and engineers like Heung-Yeung Shum. 01889 Corpus ID: 259937811; Reinforced Disentanglement for Face Swapping without Skip Connection @article{Ren2023ReinforcedDF, title={Reinforced Disentanglement for Face Swapping without Skip Connection}, author={Xiaohang Ren and Xingyu Chen and Pengfei Yao and Harry Shum and Baoyuan Wang}, journal={2023 IEEE/CVF International Conference on Computer Vision (ICCV Figure 2. 01889 (20608-20618) Online publication date: 1-Oct-2023 "Reinforced disentanglement for face swapping without skip connection," in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , 2023, pp. [33] design a person-specific network for face swapping, in Jul 16, 2023 · To fix them, we introduce a new face swap framework called 'WSC-swap' that gets rid of skip connections and uses two target encoders to respectively capture the pixel-level non-facial region attributes and the semantic non-identity attributes in the face region. The top left corner is the source image, while the other images in the first row are target images. - "Reinforced Disentanglement for Face Swapping without Skip Connection" Email: baoyuanw AT yahoo. Improving the performance of face forgery detectors often requires more identity The SOTA face swap models still suffer the problem of either target identity (i. Figure 5. The earliest work [] simply replaces the pixels in the inner face area to achieve face swapping, which can only be achieved when the source and target images have the similar angle and lighting, and the generated faces cannot keep the target expression well. ∙ Jul 16, 2023 · The SOTA face swap models still suffer the problem of either target identity (i. Jan 11, 2022 · A lightweight Identity-aware Dynamic Network (IDN) is proposed for subject-agnostic face swapping by dynamically adjusting the model parameters according to the identity information by introducing two dynamic neural network techniques, including the weights prediction and weights modulation. Find and fix vulnerabilities We show that this insufficient disentanglement is caused by two flawed designs that were commonly adopted in prior models: (1) counting on only one compressed encoder to represent both the semantic-level non-identity facial attributes(i. Ren X Chen X Yao P Shum H Wang B (2023) Reinforced Disentanglement for Face Swapping without Skip Connection 2023 IEEE/CVF International Conference on Computer Vision (ICCV) 10. ai Address: Redmond. We show that this insufficient disentanglement is caused by two flawed designs that were commonly adopted in prior models: (1) counting on only one compressed encoder to You signed in with another tab or window. Overall framework of our proposed method. A face swapping framework with a strong generalization ability should be adapted to arbitrary faces; 2). 00341 Corpus ID: 235692931; Information Bottleneck Disentanglement for Identity Swapping @article{Gao2021InformationBD, title={Information Bottleneck Disentanglement for Identity Swapping}, author={Gege Gao and Huaibo Huang and Chaoyou Fu and Zhaoyang Li and Ran He}, journal={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2021 Jun 1, 2020 · To fix them, we introduce a new face swap framework called 'WSC-swap' that gets rid of skip connections and uses two target encoders to respectively capture the pixel-level non-facial region Xiaohang Ren, Xingyu Chen, Pengfei Yao, Heung-Yeung Shum, Baoyuan Wang, Reinforced Disentanglement for Face Swapping without Skip Connection CVPR 2023 Yixuan Li, Chao Ma, Yichao Yan, Wenhan Zhu, Xiaokang Yang, 3D-Aware Face Swapping [ Paper ] [ Supp ] [ Code ] neously (1) fully preserve the face identity from the source image (2) fully preserve everything else except the iden- tity (identity-irrelevant) from the target image, and (3) en- sure the final result is both artifacts-free and photo-realistic. ai, hshum@ust. - "Information Bottleneck Disentanglement for Identity Swapping" Jul 1, 2020 · To tackle this challenge, we introduce video-to-video (V2V) face-swapping, a novel task of face-swapping that can preserve (1) the identity and expressions of the source (actor) face video and (2 Dec 31, 2019 · In this work, we propose a novel two-stage framework, called FaceShifter, for high fidelity and occlusion aware face swapping. Oct 26, 2023 · A novel Semantics and Structure-aware face swapping framework (S2Swap) that exploits semantics disentanglement and structure enhancement for high fidelity face generation and adaptively integrate semantics and structure information in a self-learning manner. g. AI 2Hong Kong University of Science and Technology renxiaohang, chenxingyu, yaopengfei@xiaobing. The attributes(e. 3D face reconstruction network. 1109/CVPR46437. Face swap results by our method. Full-text available Via our disentanglement, face swapping (FS) can be regarded as a simple arithmetic operation Face swapping can provide data support for face forgery detection, which is a very significant topic in forensics. TConv is the transposed convlutional layer. tf_mesh_renderer. Update papers. We transfer different levels of attributes by three modules. 20665-20675 PDF. - "Reinforced Disentanglement for Face Swapping without Skip Connection" Reinforced Disentanglement for Face Swapping without Skip Connection. Although previous research can leverage generative priors to produce high-resolution results, their quality can suffer from the entangled semantics of the latent space. hk, zjuwby@gmail. US First, crop and align face images from other datasets automatically so that the proportion of face occupied in the whole is similar to that of CelebA dataset and FaceForensics++ dataset. [33] design a person-specific network for face swapping, in The main difficulties in face swapping can be concluded as fol-lows: 1). Recent works heavily rely on Generative Adversarial Networks (GAN) to improve the face swap visual quality. , pose) and the pixel-level non-facial region details, which is contradictory to satisfy at the same time The SOTA face swap models still suffer the problem of either target identity (i. Note that, removing the skip connection reduces the leakage of the target face identity but hurts the preservation of its non-identity attributes (i. We use Jun 1, 2021 · This work proposes a novel information disentangling and swapping network, called InfoSwap, to extract the most expressive information for identity representation from a pre-trained face recognition model, and demonstrates that the model can provide explicit supervision for learning disentangled representations. We explicitly disentangle the latent semantics by utilizing the progressive nature of the generator Multi-view Self-supervised Disentanglement for General Image Denoising for Face Swapping without Skip Connection. Face-swapping models have been drawing attention for their compelling generation quality, but their complex architectures and loss functions often require careful To fix them, we introduce a new face swap framework called “WSC-swap” that gets rid of skip connections and uses two target encoders to respectively capture the pixel-level non-facial region attributes and the semantic non-identity attributes in the face region. We show that this insufficient disentanglement is caused by two flawed designs that were commonly adopted in prior models: (1) counting on only one compressed encoder to Jul 16, 2023 · We show that this insufficient disentanglement is caused by two flawed designs that were commonly adopted in prior models: (1) counting on only one compressed encoder to represent both the semantic-level non-identity facial attributes(i. Then, define the face swapping list siimilar to face_swap_list. [33] design a person-specific network for face swapping, in Mar 7, 2023 · Reinforced Disentanglement for Face Swapping without Skip Connection The SOTA face swap models still suffer the problem of either target iden 0 Xiaohang Ren, et al. Face swapping has been developing rapidly. , background, hair) failing to be fully preserved in the final results. - "Face Swapping as A Simple Arithmetic Operation" DOI: 10. We use the tool to generate synthetic face images. We use the open source face recognition network to extract identity features. Korshunova et al. Mar 30, 2022 · We present a novel high-resolution face swapping method using the inherent prior knowledge of a pre-trained GAN model. Unlike previous work, FSGAN is subject Face Swap. The top-left corner is the source image, while the other images in the first row are target images. Reinforced Disentanglement for Face Swapping without Skip Connection (ICCV 2023) BlendFace: Re-designing Identity Encoders for Face-Swapping ( ICCV 2023 ) [ paper ] StyleIPSB: Identity-Preserving Semantic Basis of StyleGAN for High Fidelity Face Swapping ( CVPR 2023 ) [ paper ] Jul 16, 2023 · To fix them, we introduce a new face swap framework called 'WSC-swap' that gets rid of skip connections and uses two target encoders to respectively capture the pixel-level non-facial region attributes and the semantic non-identity attributes in the face region. This paper presents a Face Swap. You signed out in another tab or window. We provide a sample code to perform face swapping given the portrait source and target images. 2021. To sum up, our contributions are: • We tackle face swapping from a new perspective of fine-grained editing, i. In contrast to previous approaches that either lack the ability to generalize to arbitrary identity or fail to preserve attributes like facial expression and gaze direction, our framework is capable of transferring the identity of an arbitrary source face into an arbitrary Jul 16, 2023 · The SOTA face swap models still suffer the problem of either target identity (i. 3D-based methods are proposed to solve these problems. e. , pose) and the pixel-level non-facial region details, which is contradictory to satisfy at the same time Oct 1, 2022 · Unlike other existing face swapping works that only use face recognition model to keep the identity similarity, we propose 3D shape-aware identity to control the face shape with the geometric Oct 27, 2022 · Face swapping can provide data support for face forgery detection, which is a very significant topic in forensics. Reload to refresh your session. 1 Face Swapping. 1109/ICCV51070. txt (source_image_name target_image_name). ) of the result face Nov 26, 2020 · Reinforced Disentanglement for Face Swapping without Skip Connection we introduce a new face swap framework called 'WSC-swap' that gets rid of skip connections and uses two target encoders to Basel Face Model 2009 (BFM09). Preprint. To further rein- force the disentanglement learning for the target encoder, Mar 9, 2022 · A novel Region-Aware Face Swapping (RAFSwap) network to achieve identity-consistent harmonious high-resolution face generation in a local-global manner and proposes a Face Mask Predictor (FMP) module incorporated with StyleGAN2 to predict identity-relevant soft facial masks in an unsupervised manner that is more practical for generating harmonioushigh-resolution faces. You switched accounts on another tab or window. , pose) and the pixel-level non-facial region details, which is contradictory to satisfy at the same time Face Swap. , Abstract. Encoders for Face-Swapping. [33] design a person-specific network for face swapping, in Dec 11, 2021 · A new face-swapping model called Smooth-Swap is proposed, which excludes complex handcrafted designs and allows fast and stable training, and builds smooth identity embedding that can provide stable gradients for identity change. [33] design a person-specific network for face swapping, in Reinforced Disentanglement for Face Swapping without Skip Connection. 20665-20675 . Paper: https DOI: 10. We present Face Swapping GAN (FSGAN) for face swapping and reenactment. expression, posture, lighting etc. Conv is the standard convlutional layer. Early face swap works [3, 10, 37, 44] mainly leverage the 3D facial model to transfer facial identity from the source into target images/videos. Our results significantly outperform previous Aug 16, 2019 · A novel recurrent neural network (RNN)-based approach for face reenactment which adjusts for both pose and expression variations and can be applied to a single image or a video sequence and uses a novel Poisson blending loss which combines Poisson optimization with perceptual loss. Face reenactment and swapping share a similar identity and attribute manipulating pattern, but most methods treat them separately, which is Figure 3. Oct 27, 2022 · 2. We show that this insufficient disentanglement is caused by two flawed designs that were commonly adopted in prior models: (1) counting on only one compressed encoder to Reinforced Disentanglement for Face Swapping without Skip Connection Xiaohang Ren, Xingyu Chen, Pengfei Yao, Heung-Yeung Shum, Baoyuan Wang ; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. In this paper, we propose a novel Semantics and Structure-aware face Swapping framework (S2Swap) that exploits semantics disentanglement Figure 1. , facial expression, hair details, etc. The appearance attributes are transferred by Jul 16, 2023 · We show that this insufficient disentanglement is caused by two flawed designs that were commonly adopted in prior models: (1) counting on only one compressed encoder to represent both the semantic-level non-identity facial attributes(i. - "Reinforced Disentanglement for Face Swapping without Skip Connection" Jul 16, 2023 · We show that this insufficient disentanglement is caused by two flawed designs that were commonly adopted in prior models: (1) counting on only one compressed encoder to represent both the semantic-level non-identity facial attributes(i. Unlike many existing face swapping works that leverage only limited information from the target image when synthesizing the swapped face, our framework, in its first stage, generates the swapped face in high-fidelity by Jul 16, 2023 · We show that this insufficient disentanglement is caused by two flawed designs that were commonly adopted in prior models: (1) counting on only one compressed encoder to represent both the semantic-level non-identity facial attributes(i. Explanation of the discriminative identities generated by InfoSwap. 01889 Corpus ID: 259937811; Reinforced Disentanglement for Face Swapping without Skip Connection @article{Ren2023ReinforcedDF, title={Reinforced Disentanglement for Face Swapping without Skip Connection}, author={Xiaohang Ren and Xingyu Chen and Pengfei Yao and Harry Shum and Baoyuan Wang}, journal={2023 IEEE/CVF International Conference on Computer Vision (ICCV Host and manage packages Security. 1109/TMM. Mar 24, 2022 · Although face swapping has attracted much attention in recent years, it remains a challenging problem. You signed in with another tab or window. ICCV 2023: 20608-20618 Reinforced Disentanglement for Face Swapping without Skip Connection. Added a link to the repository for the paper "Reinforced Disentanglement for Face Swapping without Skip Connection". , background, hair) failing to be fully preserved Dec 27, 2022 · Reinforced Disentanglement for Face Swapping without Skip Connection The SOTA face swap models still suffer the problem of either target iden 0 Xiaohang Ren, et al. , pose) and the pixel-level non-facial region details, which is contradictory to satisfy at the same time DOI: 10. Comparison of ID consistency. However, most of these methods have View Heung-Yeung Shum's profile, machine learning models, research papers, and code. This paper proposes an effective end-to-end unified framework to achieve both face reenactment and swapping, and sufficiently transfers identity and attribute based on learned disentangled representations to generate high-fidelity faces. - "Reinforced Disentanglement for Face Swapping without Skip Connection" 📚🔬 The realm of Deepfakes and style transfer has seen a significant breakthrough with the recent paper "Reinforced Disentanglement for Face Swapping without Skip Connection". [33] design a person-specific network for face swapping, in To fix them, we introduce a new face swap framework called “WSC-swap” that gets rid of skip connections and uses two target encoders to respectively capture the pixel-level non-facial region attributes and the semantic non-identity attributes in the face region. py swap results for difficult cross-age/gender cases. [12] Yue Lu, Xingyu Chen, Zhengxing Wu, Junzhi Yu, "Decoupled metric network for single-stage few-shot object detection," Jul 16, 2023 · The SOTA face swap models still suffer the problem of either target identity (i. 2024. Moreover, the representation of the identity information tends to be fixed, leading to suboptimal face Mar 30, 2022 · This work presents a novel high-resolution face swapping method using the inherent prior knowledge of a pre-trained GAN model that outperforms state-of-the-art image/video face swapping methods in terms of hallucination quality and consistency. Reinforced Disentanglement for Face Swapping without Skip Connection Xiaohang Ren 1*, Xingyu Chen , Pengfei Yao , Heung-Yeung Shum2, Baoyuan Wang1 1Xiaobing. 01889 Corpus ID: 259937811; Reinforced Disentanglement for Face Swapping without Skip Connection @article{Ren2023ReinforcedDF, title={Reinforced Disentanglement for Face Swapping without Skip Connection}, author={Xiaohang Ren and Xingyu Chen and Pengfei Yao and Harry Shum and Baoyuan Wang}, journal={2023 IEEE/CVF International Conference on Computer Vision (ICCV Jul 16, 2023 · Title: Reinforced Disentanglement for Face Swapping without Skip Connection Authors: Xiaohang Ren , Xingyu Chen , Pengfei Yao , Heung-Yeung Shum , Baoyuan Wang (Submitted on 16 Jul 2023 ( v1 ), revised 26 Jul 2023 (this version, v3), latest version 3 Aug 2023 ( v4 )) Face Swap. ResBlk is the residual convlutional block [7]. The original BFM09 model does not handle expression variations so extra expression basis are needed. Although previous research can The SOTA face swap models still suffer the problem of either target identity (i. Figure 6. Expression Basis. We show that this insufficient disentanglement is caused by two flawed designs that were commonly adopted in prior models: (1) counting on only one compressed encoder to Figure 3. Our work is designed to fix this issue. We explicitly disentangle the latent semantics by utilizing the progressive nature of the generator Reinforced Disentanglement for Face Swapping without Skip Connection we introduce a new face swap framework called 'WSC-swap' that gets rid of skip connections and uses two target encoders to DOI: 10. ). The other testing procedure is similar to Title: Reinforced Disentanglement for Face Swapping without Skip Connection; Authors: Xiaohang Ren, Xingyu Chen, Pengfei Yao, Heung-Yeung Shum, Baoyuan Wang; Abstract summary: We introduce a new face swap framework called 'WSC-swap' that gets rid of skip connections and uses two target encoders. [33] design a person-specific network for face swapping, in Jun 1, 2021 · Reinforced Disentanglement for Face Swapping without Skip Connection. , shape) being leaked or the target non-identity attributes (i. Comparison of the face-swapping results of various models. DOI: 10. It is the task of converting the source identity to the target face while preserving target attributes, thus disentangling identity and identity-unrelated (i. 5: (1) Target has bangs while source has Mar 30, 2022 · We present a novel high-resolution face swapping method using the inherent prior knowledge of a pre-trained GAN model. Please put the source images and target images in data/portrait_jpg and run python pipeline. - "Reinforced Disentanglement for Face Swapping without Skip Connection" To fix them, we introduce a new face swap framework called “WSC-swap” that gets rid of skip connec- tions and uses two target encoders to respectively capture the pixel-level non-facial region attributes and the semantic non-identity attributes in the face region. of cosine similarities between source and target identities; δs,t = arccos(σs,t). “c” is the number of output channels, and “s” denotes the up/down-sampling scales. Face Swap. Both G and E were pretrained, thus, are marked with dashed border. 01889 Corpus ID: 259937811; Reinforced Disentanglement for Face Swapping without Skip Connection @article{Ren2023ReinforcedDF, title={Reinforced Disentanglement for Face Swapping without Skip Connection}, author={Xiaohang Ren and Xingyu Chen and Pengfei Yao and Harry Shum and Baoyuan Wang}, journal={2023 IEEE/CVF International Conference on Computer Vision (ICCV DOI: 10. The FNID and NFA module details. Advanced face swapping methods have achieved appealing results. csv e362ee8f DmitryRyumin changed pull request status to open Sep 23 Jul 16, 2023 · To fix them, we introduce a new face swap framework called 'WSC-swap' that gets rid of skip connections and uses two target encoders to respectively capture the pixel-level non-facial region attributes and the semantic non-identity attributes in the face region. Along each axis we plot the performance ranking of a metric, so a polygon with a larger area means better face swap performance. Comparison of face swap performance on FF++. The existing methods leverage a large number of data samples to explore the intrinsic properties of face swapping without taking into account the semantic information of face images. ∙ Jun 11, 2021 · We propose an efficient framework, called Simple Swap (SimSwap), aiming for generalized and high fidelity face swapping. As shown, our results are noticeably better in terms of ID consistency across various target images. The pipeline of our disentangled high-resolution face swapping. 2023. We show that this insufficient disentanglement is caused by two flawed designs that were commonly adopted in prior models: (1) counting on only one compressed encoder to Jul 20, 2023 · To address this issue, we design BlendFace, a novel identity encoder for face-swapping. As for cross-hairstyle face swap, there are two situations as shown in Fig. We show that this insufficient disentanglement is caused by two flawed designs that were commonly adopted in prior models: (1) counting on only one compressed encoder to Jun 1, 2021 · This work proposes a novel information disentangling and swapping network, called InfoSwap, to extract the most expressive information for identity representation from a pre-trained face recognition model, and demonstrates that the model can provide explicit supervision for learning disentangled representations. no code implementations • ICCV 2023 • Xiaohang Ren, Xingyu Chen, Pengfei Yao, Heung-Yeung Shum, Baoyuan Wang Official inference code of “Reinforced Disentanglement for Face Swapping without Skip Connection” 11 text Figure 2. Jul 16, 2023 · We show that this insufficient disentanglement is caused by two flawed designs that were commonly adopted in prior models: (1) counting on only one compressed encoder to represent both the semantic-level non-identity facial attributes(i. 3369853 Corpus ID: 268049833; StableSwap: Stable Face Swapping in a Shared and Controllable Latent Space @article{Zhu2024StableSwapSF, title={StableSwap: Stable Face Swapping in a Shared and Controllable Latent Space}, author={Yixuan Zhu and Wenliang Zhao and Yansong Tang and Yongming Rao and Jie Zhou and Jiwen Lu}, journal={IEEE Transactions on Multimedia}, year={2024 A lightweight Identity-aware Dynamic Network (IDN) is proposed for subject-agnostic face swapping by dynamically adjusting the model parameters according to the identity information by introducing two dynamic neural network techniques, including the weights prediction and weights modulation. com Interest: To ship (embodied) AI- agents to enrich/augment human being's life and happiness Current Affiliations: Co-founder and VP of Engineering at Xiaobing. Facenet. ∙ Table 1. The key idea behind BlendFace is training face recognition models on blended images whose attributes are replaced with those of another mitigates inter-personal biases such as hairsyles. The identity of the result face should be close to the identity of the source face; 3). We present a novel high-resolution face swapping method using the inherent prior knowledge of a pre-trained GAN model. com Figure 1. G is a StyleGAN2 generator, E is an encoder, h is a style extractor network. , attribute) features is still a challenging task. , editing for swapping, and Face Swap. face component in shape and texture, enabling various ap-plications such as face beautification, hairstyle transfer, and controlling the swapping extent of face swapping. We first invert both the source and target faces to the W+ latent space of the pre-trained StyleGAN G by the pSp encoder, then learn a structure transfer direction by encoding the source and target landmarks. It is the task of converting the source identity to the target face while Reinforced Disentanglement for Face Swapping without Skip Connection The SOTA face swap models still suffer the problem of either target iden 0 Xiaohang Ren, et al. Reinforced Disentanglement for Face Swapping without Skip Connection Aug 26, 2023 · Reinforced Disentanglement for Face Swapping without Skip Connection Authors:Xiaohang Ren, Xingyu Chen, Pengfei Yao, Heung-Yeung Shum, Baoyuan Wang The SOTA face swap models still suffer the problem of either target identity (i. kt gh vk rh jr lk oz nm si rq