Portrait_Generation
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ReliTalk: Relightable Talking Portrait Generation from a Single Video
2023-09-05 17:59:42Haonan Qiu, Zhaoxi Chen, Yuming Jiang, Hang Zhou, Xiangyu Fan, Lei Yang, Wayne Wu, Ziwei LiuAbstract
Recent years have witnessed great progress in creating vivid audio-driven portraits from monocular videos. However, how to seamlessly adapt the created video avatars to other scenarios with different backgrounds and lighting conditions remains unsolved. On the other hand, existing relighting studies mostly rely on dynamically lighted or multi-view data, which are too expensive for creating video portraits. To bridge this gap, we propose ReliTalk, a novel framework for relightable audio-driven talking portrait generation from monocular videos. Our key insight is to decompose the portrait's reflectance from implicitly learned audio-driven facial normals and images. Specifically, we involve 3D facial priors derived from audio features to predict delicate normal maps through implicit functions. These initially predicted normals then take a crucial part in reflectance decomposition by dynamically estimating the lighting condition of the given video. Moreover, the stereoscopic face representation is refined using the identity-consistent loss under simulated multiple lighting conditions, addressing the ill-posed problem caused by limited views available from a single monocular video. Extensive experiments validate the superiority of our proposed framework on both real and synthetic datasets. Our code is released in this https URL.
Abstract (translated)
近年来,在从单目视频创建生动音频驱动肖像方面取得了巨大的进展。然而,如何无缝地将创建的视频Avatar适应其他背景和照明条件不同的场景仍然未解决。另一方面,现有的照明研究大多依赖于动态照明或多视图数据,这些对于创建视频肖像来说太贵了。为了解决这个问题,我们提出了ReliTalk,一个可以从单目视频创建可照明音频驱动对话肖像的新框架。我们的关键发现是分解肖像的反射从 implicitly learned 音频驱动面部正常和图像。具体来说,我们涉及从音频特征得出的3D面部先验以通过隐含函数预测脆弱的面部映射。这些起初预测的面部正常随后通过动态估计给定视频的照明条件来关键地参与反射分解。此外,使用模拟多种照明条件相同的损失改进立体面部表示,解决了由单目视频有限视角带来的困难问题。广泛的实验验证了我们提出的框架在真实和合成数据集上的优越性。我们的代码在此httpsURL上发布。
URL
https://arxiv.org/abs/2309.02434
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AniPortraitGAN: Animatable 3D Portrait Generation from 2D Image Collections
2023-09-05 12:44:57Yue Wu, Sicheng Xu, Jianfeng Xiang, Fangyun Wei, Qifeng Chen, Jiaolong Yang, Xin TongAbstract
Previous animatable 3D-aware GANs for human generation have primarily focused on either the human head or full body. However, head-only videos are relatively uncommon in real life, and full body generation typically does not deal with facial expression control and still has challenges in generating high-quality results. Towards applicable video avatars, we present an animatable 3D-aware GAN that generates portrait images with controllable facial expression, head pose, and shoulder movements. It is a generative model trained on unstructured 2D image collections without using 3D or video data. For the new task, we base our method on the generative radiance manifold representation and equip it with learnable facial and head-shoulder deformations. A dual-camera rendering and adversarial learning scheme is proposed to improve the quality of the generated faces, which is critical for portrait images. A pose deformation processing network is developed to generate plausible deformations for challenging regions such as long hair. Experiments show that our method, trained on unstructured 2D images, can generate diverse and high-quality 3D portraits with desired control over different properties.
Abstract (translated)
之前的人类生成器使用的可模拟3D感知GAN主要关注人类头部或整个身体的生成。然而,在现实生活中,只生成头部视频较为罕见,而且通常不会涉及到面部表情控制和生成高质量结果的挑战。为了适用于视频虚拟角色,我们提出了一种可模拟3D感知的GAN,能够生成可控制面部表情、头部姿势和肩膀运动的肖像图像。这是一个在不使用3D或视频数据的情况下从无向图表示学习到的生成模型。针对新的任务,我们基于生成光度聚类表示来学习生成器和对抗器,并配备了可学习面部表情和头部姿势变形。我们提出了双摄像头渲染和对抗学习方案来改进生成面部质量,这对于肖像图像至关重要。我们还开发了 pose变形处理网络,用于生成令人信服的变形,例如长发。实验表明,我们的方法在无向图表示学习中训练,能够生成各种高质量的3D肖像,并能够对不同属性进行定制控制。
URL
https://arxiv.org/abs/2309.02186
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FaceChain: A Playground for Identity-Preserving Portrait Generation
2023-08-28 02:20:44Yang Liu, Cheng Yu, Lei Shang, Ziheng Wu, Xingjun Wang, Yuze Zhao, Lin Zhu, Chen Cheng, Weitao Chen, Chao Xu, Haoyu Xie, Yuan Yao, Wenmeng Zhou, Yingda Chen, Xuansong Xie, Baigui SunAbstract
Recent advancement in personalized image generation have unveiled the intriguing capability of pre-trained text-to-image models on learning identity information from a collection of portrait images. However, existing solutions can be vulnerable in producing truthful details, and usually suffer from several defects such as (i) The generated face exhibit its own unique characteristics, \ie facial shape and facial feature positioning may not resemble key characteristics of the input, and (ii) The synthesized face may contain warped, blurred or corrupted regions. In this paper, we present FaceChain, a personalized portrait generation framework that combines a series of customized image-generation model and a rich set of face-related perceptual understanding models (\eg, face detection, deep face embedding extraction, and facial attribute recognition), to tackle aforementioned challenges and to generate truthful personalized portraits, with only a handful of portrait images as input. Concretely, we inject several SOTA face models into the generation procedure, achieving a more efficient label-tagging, data-processing, and model post-processing compared to previous solutions, such as DreamBooth ~\cite{ruiz2023dreambooth} , InstantBooth ~\cite{shi2023instantbooth} , or other LoRA-only approaches ~\cite{hu2021lora} . Through the development of FaceChain, we have identified several potential directions to accelerate development of Face/Human-Centric AIGC research and application. We have designed FaceChain as a framework comprised of pluggable components that can be easily adjusted to accommodate different styles and personalized needs. We hope it can grow to serve the burgeoning needs from the communities. FaceChain is open-sourced under Apache-2.0 license at \url{this https URL}.
Abstract (translated)
最近的个性化图像生成技术的进步揭示了预训练文本到图像模型从一组肖像图像中学习身份信息的独特能力。然而,现有的解决方案在生成真实细节方面可能存在脆弱性,通常会出现多个缺陷,例如(i)生成的面部呈现其自身的独特特征, \ie 面部形状和面部特征位置可能不像输入的关键特征相似,(ii)合成的面部可能包含扭曲、模糊或失真的区域。在本文中,我们介绍了 FaceChain,一个个性化的肖像生成框架,它结合了一系列定制的图像生成模型和大量的面部相关感知理解模型,以解决上述挑战并生成只有少量肖像图像输入的真实个性化肖像。具体而言,我们注入 several SOTA 面部模型到生成过程,比过去的解决方案更高效地进行标签标注、数据处理和模型后处理,相比 Dreambooth ~\cite{ruiz2023dreambooth}、Instantbooth ~\cite{shi2023Instantbooth} 或 other LoRA-only approaches ~\cite{hu2021lora} 等方案更加高效。通过开发 FaceChain,我们识别了几个可能的方向,以加速 Face/人类中心 AIGC 研究和应用程序的发展。我们设计了 FaceChain,作为一个可插拔组件组成的框架,可以轻松适应不同的风格和个性化需求。我们希望它能够成长来满足社区不断增长的需求。FaceChain 采用 Apache-2.0 许可证开源。
URL
https://arxiv.org/abs/2308.14256
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MODA: Mapping-Once Audio-driven Portrait Animation with Dual Attentions
2023-07-19 14:45:11Yunfei Liu, Lijian Lin, Fei Yu, Changyin Zhou, Yu LiAbstract
Audio-driven portrait animation aims to synthesize portrait videos that are conditioned by given audio. Animating high-fidelity and multimodal video portraits has a variety of applications. Previous methods have attempted to capture different motion modes and generate high-fidelity portrait videos by training different models or sampling signals from given videos. However, lacking correlation learning between lip-sync and other movements (e.g., head pose/eye blinking) usually leads to unnatural results. In this paper, we propose a unified system for multi-person, diverse, and high-fidelity talking portrait generation. Our method contains three stages, i.e., 1) Mapping-Once network with Dual Attentions (MODA) generates talking representation from given audio. In MODA, we design a dual-attention module to encode accurate mouth movements and diverse modalities. 2) Facial composer network generates dense and detailed face landmarks, and 3) temporal-guided renderer syntheses stable videos. Extensive evaluations demonstrate that the proposed system produces more natural and realistic video portraits compared to previous methods.
Abstract (translated)
Audio驱动的肖像动画旨在生成由给定音频条件生成的肖像视频。动画高保真的多媒质肖像具有多种应用。先前的方法曾试图捕捉不同运动模式并生成高保真的肖像视频,通过训练不同模型或从给定视频中采样信号来实现。然而,缺乏 lips同步和其他运动(例如头部姿势和眨眼)的相关性学习通常会导致不自然的结果。在本文中,我们提出了一种多人、多样化和高保真的对话肖像生成统一系统。我们的方法和有三个阶段,即1)基于双重注意力的一次性网络(MODA)从给定音频生成对话表示。在MODA中,我们设计了一个双重注意力模块来编码准确的口部运动和多种模式。2)面部构建网络生成密集且详细的面部地标,3)时间引导渲染合成稳定视频。广泛的评估表明,与先前的方法相比,我们提出的系统生成更自然、真实的视频肖像。
URL
https://arxiv.org/abs/2307.10008
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Efficient Text-Guided 3D-Aware Portrait Generation with Score Distillation Sampling on Distribution
2023-06-03 11:08:38Yiji Cheng, Fei Yin, Xiaoke Huang, Xintong Yu, Jiaxiang Liu, Shikun Feng, Yujiu Yang, Yansong TangAbstract
Text-to-3D is an emerging task that allows users to create 3D content with infinite possibilities. Existing works tackle the problem by optimizing a 3D representation with guidance from pre-trained diffusion models. An apparent drawback is that they need to optimize from scratch for each prompt, which is computationally expensive and often yields poor visual fidelity. In this paper, we propose DreamPortrait, which aims to generate text-guided 3D-aware portraits in a single-forward pass for efficiency. To achieve this, we extend Score Distillation Sampling from datapoint to distribution formulation, which injects semantic prior into a 3D distribution. However, the direct extension will lead to the mode collapse problem since the objective only pursues semantic alignment. Hence, we propose to optimize a distribution with hierarchical condition adapters and GAN loss regularization. For better 3D modeling, we further design a 3D-aware gated cross-attention mechanism to explicitly let the model perceive the correspondence between the text and the 3D-aware space. These elaborated designs enable our model to generate portraits with robust multi-view semantic consistency, eliminating the need for optimization-based methods. Extensive experiments demonstrate our model's highly competitive performance and significant speed boost against existing methods.
Abstract (translated)
文本到三维是一项新兴任务,它允许用户以无限的可能创造三维内容。现有的工作通过从训练好的扩散模型中指导优化三维表示来解决这个问题。一个明显的缺点是他们需要为每个提示都重新优化,这是计算代价高昂的,并且通常会导致视觉效果不佳。在本文中,我们提出了梦想肖像,它旨在以高效的方式从文本引导的三维意识肖像中生成。为了实现这个目标,我们将评分蒸馏采样扩展到分布 formulation,将语义先验注入到三维分布中。然而,直接扩展将会导致模式崩溃问题,因为目标只是追求语义匹配。因此,我们提议使用分层条件适配器和GAN损失 Regularization 来优化分布。为了提供更好的三维建模,我们还设计了三维意识闭路交叉注意力机制,以明确让模型感知文本和三维意识空间之间的对应关系。这些 elaborate 的设计使我们能够生成具有稳健多视角语义一致性的肖像,从而不再需要基于优化的方法。广泛的实验证明了我们的模型的高竞争力表现以及与现有方法的重大速度提升。
URL
https://arxiv.org/abs/2306.02083
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Few-shots Portrait Generation with Style Enhancement and Identity Preservation
2023-03-01 10:02:12Runchuan Zhu, Naye Ji, Youbing Zhao, Fan ZhangAbstract
Nowadays, the wide application of virtual digital human promotes the comprehensive prosperity and development of digital culture supported by digital economy. The personalized portrait automatically generated by AI technology needs both the natural artistic style and human sentiment. In this paper, we propose a novel StyleIdentityGAN model, which can ensure the identity and artistry of the generated portrait at the same time. Specifically, the style-enhanced module focuses on artistic style features decoupling and transferring to improve the artistry of generated virtual face images. Meanwhile, the identity-enhanced module preserves the significant features extracted from the input photo. Furthermore, the proposed method requires a small number of reference style data. Experiments demonstrate the superiority of StyleIdentityGAN over state-of-art methods in artistry and identity effects, with comparisons done qualitatively, quantitatively and through a perceptual user study. Code has been released on Github3.
Abstract (translated)
Nowadays, the widespread application of virtual digital human promotes the comprehensive prosperity and development of digital culture supported by digital economy. The personalized portrait automatically generated by AI technology needs both the natural artistic style and human sentiment. In this paper, we propose a novel StyleIdentityGAN model, which can ensure the identity and artistry of the generated portrait at the same time. Specifically, the style-enhanced module focuses on artistic style features decoupling and transferred to improve the artistry of generated virtual face images. Meanwhile, the identity-enhanced module preserves the significant features extracted from the input photo. Furthermore, the proposed method requires a small number of reference style data. experiments demonstrate the superiority of StyleIdentityGAN over state-of-art methods in artistry and identity effects, with comparisons done qualitatively, quantitatively and through a perceptual user study. Code has been released on Github3.
URL
https://arxiv.org/abs/2303.00377
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One-Shot Face Video Re-enactment using Hybrid Latent Spaces of StyleGAN2
2023-02-15 18:34:15Trevine Oorloff, Yaser YacoobAbstract
While recent research has progressively overcome the low-resolution constraint of one-shot face video re-enactment with the help of StyleGAN's high-fidelity portrait generation, these approaches rely on at least one of the following: explicit 2D/3D priors, optical flow based warping as motion descriptors, off-the-shelf encoders, etc., which constrain their performance (e.g., inconsistent predictions, inability to capture fine facial details and accessories, poor generalization, artifacts). We propose an end-to-end framework for simultaneously supporting face attribute edits, facial motions and deformations, and facial identity control for video generation. It employs a hybrid latent-space that encodes a given frame into a pair of latents: Identity latent, $\mathcal{W}_{ID}$, and Facial deformation latent, $\mathcal{S}_F$, that respectively reside in the $W+$ and $SS$ spaces of StyleGAN2. Thereby, incorporating the impressive editability-distortion trade-off of $W+$ and the high disentanglement properties of $SS$. These hybrid latents employ the StyleGAN2 generator to achieve high-fidelity face video re-enactment at $1024^2$. Furthermore, the model supports the generation of realistic re-enactment videos with other latent-based semantic edits (e.g., beard, age, make-up, etc.). Qualitative and quantitative analyses performed against state-of-the-art methods demonstrate the superiority of the proposed approach.
Abstract (translated)
近年来,通过使用风格GAN的高保真肖像生成技术,逐渐克服了一次性面部视频重制低分辨率的限制。但这些方法至少依赖于以下一种: explicit 2D/3D priors,基于光学流的扭曲作为运动描述器,常见的编码器,等等,这些限制影响了其表现(例如,不一致的预测,无法捕捉 fine facial details 和配件, poor generalization, 人为错误)。我们提出了一个端到端的框架,可以同时支持面部属性编辑、面部运动和变形,以及视频生成时的面部身份控制。它使用了一个混合的隐状态空间,将给定帧编码为一对隐状态:身份隐状态, $mathcal{W}_{ID}$,和面部变形隐状态, $mathcal{S}_F$,分别位于StyleGAN2的$W+$和$SS$空间中。因此,结合了$W+$的令人印象深刻编辑能力与$SS$的高分离能力。这些混合隐状态使用StyleGAN2生成器来实现1024^2级的高保真面部视频重制。此外,模型还支持与其他隐状态语义编辑相关的真实重制视频生成(例如,胡须、年龄、化妆等)。与当前最佳方法进行比较的定性和定量分析表明,我们提出的方法具有优越性。
URL
https://arxiv.org/abs/2302.07848
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Make Your Brief Stroke Real and Stereoscopic: 3D-Aware Simplified Sketch to Portrait Generation
2023-02-14 06:28:42Yasheng Sun, Qianyi Wu, Hang Zhou, Kaisiyuan Wang, Tianshu Hu, Chen-Chieh Liao, Dongliang He, Jingtuo Liu, Errui Ding, Jingdong Wang, Shio Miyafuji, Ziwei Liu, Hideki KoikeAbstract
Creating the photo-realistic version of people sketched portraits is useful to various entertainment purposes. Existing studies only generate portraits in the 2D plane with fixed views, making the results less vivid. In this paper, we present Stereoscopic Simplified Sketch-to-Portrait (SSSP), which explores the possibility of creating Stereoscopic 3D-aware portraits from simple contour sketches by involving 3D generative models. Our key insight is to design sketch-aware constraints that can fully exploit the prior knowledge of a tri-plane-based 3D-aware generative model. Specifically, our designed region-aware volume rendering strategy and global consistency constraint further enhance detail correspondences during sketch encoding. Moreover, in order to facilitate the usage of layman users, we propose a Contour-to-Sketch module with vector quantized representations, so that easily drawn contours can directly guide the generation of 3D portraits. Extensive comparisons show that our method generates high-quality results that match the sketch. Our usability study verifies that our system is greatly preferred by user.
Abstract (translated)
创造绘制的肖像的逼真版本对于多种娱乐用途是有用的。现有的研究只能从固定视角下在2D平面上生成肖像,导致结果不够生动。在本文中,我们提出了立体简化 Sketch-to-Portrait (SSSP),该方法探索了从简单的轮廓 Sketch 创建立体3D感知肖像的可能性。我们的关键发现是设计 Sketch aware 约束,充分利用基于三平面的3D感知生成模型的先前知识。具体来说,我们设计的区域感知体积渲染策略和全局一致性约束在 Sketch 编码期间进一步增强细节对应关系。此外,为了便于一般用户使用,我们提出了轮廓到 Sketch 模块,使用向量量化表示,使得易于绘制的轮廓可以直接影响3D肖像的生成。广泛的比较表明,我们的方法生成了与 Sketch 匹配高质量的结果。我们的使用研究证实了用户对我们系统的巨大偏好。
URL
https://arxiv.org/abs/2302.06857
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Explicitly Controllable 3D-Aware Portrait Generation
2022-09-12 17:40:08Junshu Tang, Bo Zhang, Binxin Yang, Ting Zhang, Dong Chen, Lizhuang Ma, Fang WenAbstract
In contrast to the traditional avatar creation pipeline which is a costly process, contemporary generative approaches directly learn the data distribution from photographs and the state of the arts can now yield highly photo-realistic images. While plenty of works attempt to extend the unconditional generative models and achieve some level of controllability, it is still challenging to ensure multi-view consistency, especially in large poses. In this work, we propose a 3D portrait generation network that produces 3D consistent portraits while being controllable according to semantic parameters regarding pose, identity, expression and lighting. The generative network uses neural scene representation to model portraits in 3D, whose generation is guided by a parametric face model that supports explicit control. While the latent disentanglement can be further enhanced by contrasting images with partially different attributes, there still exists noticeable inconsistency in non-face areas, e.g., hair and background, when animating expressions. We solve this by proposing a volume blending strategy in which we form a composite output by blending the dynamic and static radiance fields, with two parts segmented from the jointly learned semantic field. Our method outperforms prior arts in extensive experiments, producing realistic portraits with vivid expression in natural lighting when viewed in free viewpoint. The proposed method also demonstrates generalization ability to real images as well as out-of-domain cartoon faces, showing great promise in real applications. Additional video results and code will be available on the project webpage.
Abstract (translated)
URL
https://arxiv.org/abs/2209.05434
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Injecting 3D Perception of Controllable NeRF-GAN into StyleGAN for Editable Portrait Image Synthesis
2022-07-21 01:41:54Jeong-gi Kwak, Yuanming Li, Dongsik Yoon, Donghyeon Kim, David Han, Hanseok KoAbstract
Over the years, 2D GANs have achieved great successes in photorealistic portrait generation. However, they lack 3D understanding in the generation process, thus they suffer from multi-view inconsistency problem. To alleviate the issue, many 3D-aware GANs have been proposed and shown notable results, but 3D GANs struggle with editing semantic attributes. The controllability and interpretability of 3D GANs have not been much explored. In this work, we propose two solutions to overcome these weaknesses of 2D GANs and 3D-aware GANs. We first introduce a novel 3D-aware GAN, SURF-GAN, which is capable of discovering semantic attributes during training and controlling them in an unsupervised manner. After that, we inject the prior of SURF-GAN into StyleGAN to obtain a high-fidelity 3D-controllable generator. Unlike existing latent-based methods allowing implicit pose control, the proposed 3D-controllable StyleGAN enables explicit pose control over portrait generation. This distillation allows direct compatibility between 3D control and many StyleGAN-based techniques (e.g., inversion and stylization), and also brings an advantage in terms of computational resources. Our codes are available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2207.10257
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Evolutionary latent space search for driving human portrait generation
2022-04-25 18:00:49Benjamín Machín, Sergio Nesmachnow, Jamal ToutouhAbstract
This article presents an evolutionary approach for synthetic human portraits generation based on the latent space exploration of a generative adversarial network. The idea is to produce different human face images very similar to a given target portrait. The approach applies StyleGAN2 for portrait generation and FaceNet for face similarity evaluation. The evolutionary search is based on exploring the real-coded latent space of StyleGAN2. The main results over both synthetic and real images indicate that the proposed approach generates accurate and diverse solutions, which represent realistic human portraits. The proposed research can contribute to improving the security of face recognition systems.
Abstract (translated)
URL
https://arxiv.org/abs/2204.11887
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Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer
2022-03-24 17:57:11Shuai Yang, Liming Jiang, Ziwei Liu, Chen Change LoyAbstract
Recent studies on StyleGAN show high performance on artistic portrait generation by transfer learning with limited data. In this paper, we explore more challenging exemplar-based high-resolution portrait style transfer by introducing a novel DualStyleGAN with flexible control of dual styles of the original face domain and the extended artistic portrait domain. Different from StyleGAN, DualStyleGAN provides a natural way of style transfer by characterizing the content and style of a portrait with an intrinsic style path and a new extrinsic style path, respectively. The delicately designed extrinsic style path enables our model to modulate both the color and complex structural styles hierarchically to precisely pastiche the style example. Furthermore, a novel progressive fine-tuning scheme is introduced to smoothly transform the generative space of the model to the target domain, even with the above modifications on the network architecture. Experiments demonstrate the superiority of DualStyleGAN over state-of-the-art methods in high-quality portrait style transfer and flexible style control.
Abstract (translated)
URL
https://arxiv.org/abs/2203.13248
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DrawingInStyles: Portrait Image Generation and Editing with Spatially Conditioned StyleGAN
2022-03-05 14:54:07Wanchao Su, Hui Ye, Shu-Yu Chen, Lin Gao, Hongbo FuAbstract
The research topic of sketch-to-portrait generation has witnessed a boost of progress with deep learning techniques. The recently proposed StyleGAN architectures achieve state-of-the-art generation ability but the original StyleGAN is not friendly for sketch-based creation due to its unconditional generation nature. To address this issue, we propose a direct conditioning strategy to better preserve the spatial information under the StyleGAN framework. Specifically, we introduce Spatially Conditioned StyleGAN (SC-StyleGAN for short), which explicitly injects spatial constraints to the original StyleGAN generation process. We explore two input modalities, sketches and semantic maps, which together allow users to express desired generation results more precisely and easily. Based on SC-StyleGAN, we present DrawingInStyles, a novel drawing interface for non-professional users to easily produce high-quality, photo-realistic face images with precise control, either from scratch or editing existing ones. Qualitative and quantitative evaluations show the superior generation ability of our method to existing and alternative solutions. The usability and expressiveness of our system are confirmed by a user study.
Abstract (translated)
URL
https://arxiv.org/abs/2203.02762
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Semantic-Aware Implicit Neural Audio-Driven Video Portrait Generation
2022-01-19 18:54:41Xian Liu, Yinghao Xu, Qianyi Wu, Hang Zhou, Wayne Wu, Bolei ZhouAbstract
Animating high-fidelity video portrait with speech audio is crucial for virtual reality and digital entertainment. While most previous studies rely on accurate explicit structural information, recent works explore the implicit scene representation of Neural Radiance Fields (NeRF) for realistic generation. In order to capture the inconsistent motions as well as the semantic difference between human head and torso, some work models them via two individual sets of NeRF, leading to unnatural results. In this work, we propose Semantic-aware Speaking Portrait NeRF (SSP-NeRF), which creates delicate audio-driven portraits using one unified set of NeRF. The proposed model can handle the detailed local facial semantics and the global head-torso relationship through two semantic-aware modules. Specifically, we first propose a Semantic-Aware Dynamic Ray Sampling module with an additional parsing branch that facilitates audio-driven volume rendering. Moreover, to enable portrait rendering in one unified neural radiance field, a Torso Deformation module is designed to stabilize the large-scale non-rigid torso motions. Extensive evaluations demonstrate that our proposed approach renders more realistic video portraits compared to previous methods. Project page: this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2201.07786
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MUSE: Illustrating Textual Attributes by Portrait Generation
2020-11-09 21:05:21Xiaodan Hu, Pengfei Yu, Kevin Knight, Heng Ji, Bo Li, Honghui ShiAbstract
We propose a novel approach, MUSE, to illustrate textual attributes visually via portrait generation. MUSE takes a set of attributes written in text, in addition to facial features extracted from a photo of the subject as input. We propose 11 attribute types to represent inspirations from a subject's profile, emotion, story, and environment. We propose a novel stacked neural network architecture by extending an image-to-image generative model to accept textual attributes. Experiments show that our approach significantly outperforms several state-of-the-art methods without using textual attributes, with Inception Score score increased by 6% and Fréchet Inception Distance (FID) score decreased by 11%, respectively. We also propose a new attribute reconstruction metric to evaluate whether the generated portraits preserve the subject's attributes. Experiments show that our approach can accurately illustrate 78% textual attributes, which also help MUSE capture the subject in a more creative and expressive way.
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URL
https://arxiv.org/abs/2011.04761
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