Given a speaker's speech, it is interesting to see if it is possible to generate this speaker's face. One main challenge in this task is to alleviate the natural mismatch between face and speech. To this end, in this paper, we propose a novel Attention-based Residual Speech Portrait Model (AR-SPM) by introducing the ideal of the residual into a hybrid encoder-decoder architecture, where face prior features are merged with the output of speech encoder to form the final face feature. In particular, we innovatively establish a tri-item loss function, which is a weighted linear combination of the L2-norm, L1-norm and negative cosine loss, to train our model by comparing the final face feature and true face feature. Evaluation on AVSpeech dataset shows that our proposed model accelerates the convergence of training, outperforms the state-of-the-art in terms of quality of the generated face, and achieves superior recognition accuracy of gender and age compared with the ground truth.
Animating portraits using speech has received growing attention in recent years, with various creative and practical use cases. An ideal generated video should have good lip sync with the audio, natural facial expressions and head motions, and high frame quality. In this work, we present SPACE, which uses speech and a single image to generate high-resolution, and expressive videos with realistic head pose, without requiring a driving video. It uses a multi-stage approach, combining the controllability of facial landmarks with the high-quality synthesis power of a pretrained face generator. SPA…
To the best of our knowledge, we first present a live system that generates personalized photorealistic talking-head animation only driven by audio signals at over 30 fps. Our system contains three stages. The first stage is a deep neural network that extracts deep audio features along with a manifold projection to project the features to the target person's speech space. In the second stage, we learn facial dynamics and motions from the projected audio features. The predicted motions include head poses and upper body motions, where the former is generated by an autoregressive probabilistic mo…
University of Augsburg（奥格斯堡大学）；Imperial College London（帝国理工学院）
We present a novel source separation model to decompose asingle-channel speech signal into two speech segments belonging to two different speakers. The proposed model is a neural network based on residual blocks, and uses learnt speaker embeddings created from additional clean context recordings of the two speakers as input to assist in attributing the different time-frequency bins to the two speakers. In experiments, we show that the proposed model yields good performance in the source separation task, and outperforms the state-of-the-art baselines. Specifically, separating speech from the ch…
The Chinese University of Hong Kong（香港中文大学）；Monash University（莫纳什大学）；SenseTime Research（商汤科技研究院）；Sensetime（商汤科技（此处原英文为“sensetime”，结合上下文“wuwenyan@sensetime.com”推测为“商汤科技”，但严格按“机构粒度”提取，原文本中“SenseTime Research”已单独列出，此处“sensetime”视为同一机构））
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 …
Data61/CSIRO（联邦科学与工业研究组织Data61）；Australian National University（澳大利亚国立大学）
Given an artistic portrait, recovering the latent photorealistic face that preserves the subject's identity is challenging because the facial details are often distorted or fully lost in artistic portraits. We develop an Identity-preserving Face Recovery from Portraits (IFRP) method that utilizes a Style Removal network (SRN) and a Discriminative Network (DN). Our SRN, composed of an autoencoder with residual block-embedded skip connections, is designed to transfer feature maps of stylized images to the feature maps of the corresponding photorealistic faces. Owing to the Spatial Transformer Ne…
See the Speaker: Crafting High-Resolution Talking Faces from Speech with Prior Guidance and Region Refinement
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Speech and Acoustic Laboratory, Joy Future Academy, Jingdong Corporation（语音与声学实验室、Joy Future Academy、京东公司）；Aarhus University（阿arhus大学）
Australian National University（澳大利亚国立大学）；Data61/CSIRO（Data61/联邦科学与工业研究组织）
Recovering a photorealistic face from an artistic portrait is a challenging task since crucial facial details are often distorted or completely lost in artistic compositions. To handle this loss, we propose an Attribute-guided Face Recovery from Portraits (AFRP) that utilizes a Face Recovery Network (FRN) and a Discriminative Network (DN). FRN consists of an autoencoder with residual block-embedded skip-connections and incorporates facial attribute vectors into the feature maps of input portraits at the bottleneck of the autoencoder. DN has multiple convolutional and fully-connected layers, an…
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Istanbul Technical University（伊斯坦布尔理工大学）；Carnegie Mellon University（卡内基梅隆大学）
In the portrait matting, the goal is to predict an alpha matte that identifies the effect of each pixel on the foreground subject. Traditional approaches and most of the existing works utilized an additional input, e.g., trimap, background image, to predict alpha matte. However, (1) providing additional input is not always practical, and (2) models are too sensitive to these additional inputs. To address these points, in this paper, we introduce an additional input-free approach to perform portrait matting. We divide the task into two subtasks, segmentation and alpha matte prediction. We first…
Tianjin University（天津大学）；Chinese Academy of Social Sciences（中国社会科学院）；the Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）
Previous works derive personalized speech features by training the model on a large dataset composed of his/her audio sounds. It was reported that face information has a strong link with the speech sound. Thus in this work, we innovatively extract personalized speech features from human faces to synthesize personalized speech using neural vocoder. A Face-based Residual Personalized Speech Synthesis Model (FR-PSS) containing a speech encoder, a speech synthesizer and a face encoder is designed for PSS. In this model, by designing two speech priors, a residual-guided strategy is introduced to gu…
Mamba-Enhanced Implicit Motion Learning for Audio-Driven Portrait Animation
Fujian Provincial Natural Science Foundation of China（福建省自然科学基金委员会）；Giant Interactive Group Inc.（巨匠互动集团有限公司）；National Natural Science Foundation of China（国家自然科学基金委员会）
With the increasing availability of large digitized fine art collections, automated analysis and classification of paintings is becoming an interesting area of research. However, due to domain specificity, implicit subjectivity, and pervasive nuances that vaguely separate art movements, analyzing art using machine learning techniques poses significant challenges. Residual networks, or variants thereof, are one the most popular tools for image classification tasks, which can extract relevant features for well-defined classes. In this case study, we focus on the classification of a selected pain…
Fake portrait video generation techniques have been posing a new threat to the society with photorealistic deep fakes for political propaganda, celebrity imitation, forged evidences, and other identity related manipulations. Following these generation techniques, some detection approaches have also been proved useful due to their high classification accuracy. Nevertheless, almost no effort was spent to track down the source of deep fakes. We propose an approach not only to separate deep fakes from real videos, but also to discover the specific generative model behind a deep fake. Some pure dee…
Audio-Driven Talking Face Generation with Blink Embedding and Hash Grid Landmarks Encoding
Department of Control Science and Engineering, University of Shanghai for Science and Technology（控制科学与工程系，上海科学技术大学）；School of Psychology and Neuroscience, University of Glasgow（心理学与神经科学学院，格拉斯哥大学）
Encoder-decoder based architecture has been widely used in the generator of generative adversarial networks for facial manipulation. However, we observe that the current architecture fails to recover the input image color, rich facial details such as skin color or texture and introduces artifacts as well. In this paper, we present a novel method named SARGAN that addresses the above-mentioned limitations from three perspectives. First, we employed spatial attention-based residual block instead of vanilla residual blocks to properly capture the expression-related features to be changed while ke…
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of Maryland（马里兰大学）
Understanding the underlying relationship between tongue and oropharyngeal muscle deformation seen in tagged-MRI and intelligible speech plays an important role in advancing speech motor control theories and treatment of speech related-disorders. Because of their heterogeneous representations, however, direct mapping between the two modalities -- i.e., two-dimensional (mid-sagittal slice) plus time tagged-MRI sequence and its corresponding one-dimensional waveform -- is not straightforward. Instead, we resort to two-dimensional spectrograms as an intermediate representation, which contains bot…
Multimodal Diffusion Transformer with Memory Bank for Scalable Long-Duration Talking Video Generation
South China University of Technology（南方科技大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Beijing National Research Center for Information Science and Technology, Tsinghua University（北京信息科学研究中心，清华大学）；Department of Automation, BNRist, Tsinghua University（清华大学自动化系，北京信息科学研究中心）
We propose an end-to-end deep learning architecture for word-level visual speech recognition. The system is a combination of spatiotemporal convolutional, residual and bidirectional Long Short-Term Memory networks. We train and evaluate it on the Lipreading In-The-Wild benchmark, a challenging database of 500-size target-words consisting of 1.28sec video excerpts from BBC TV broadcasts. The proposed network attains word accuracy equal to 83.0, yielding 6.8 absolute improvement over the current state-of-the-art, without using information about word boundaries during training or testing.
