The emergence of photo-realistic deepfakes on a large scale has become a significant societal concern, which has garnered considerable attention from the research community. Several recent studies have identified the critical issue of “temporal inconsistency” resulting from the frame reassembling process of deepfake generation techniques. However, due to the lack of task-specific design, the spatio-temporal modeling of current methods remains insufficient in three critical aspects: 1) inapparent temporal changes are prone to be undermined compared to abundant spatial cues; 2) minor inconsistent regions are often concealed by motions with greater amplitude during downsampling; 3) capturing both transient inconsistencies and persistent motions simultaneously remains a significant challenge. In this paper, we propose a novel Dual-Modality Co-Learning framework tailored for these characteristics, which achieves more effectual deepfake detection with complementary information from RGB and optical flow modalities. In particular, we designed a Multi-Scale Motion Regularization module to encourage the network to equally prioritize both the significant spatial cues and the subtle temporal facial motion cues. Additionally, we developed a Multi-Span Cross-Attention module to effectively integrate the information from both RGB and optical flow modalities and improve the detection accuracy with multi-span predictions. Extensive experiments validate the effectiveness our ideas and demonstrate the superior performance of our approach.

Generating expressive talking heads is essential for creating virtual humans. However, existing one- or few-shot methods focus on lip-sync and head motion, ignoring the emotional expressions that make talking faces realistic. In this paper, we propose the Granularly Controlled Audio-Visual Talking Heads (GC-AVT), which controls lip movements, head poses, and facial expressions of a talking head in a granular manner. Our insight is to decouple the audio-visual driving sources through prior-based pre-processing designs. Detailedly, we disassemble the driving image into three complementary parts including: 1) a cropped mouth that facilitates lip-sync; 2) a masked head that implicitly learns pose; and 3) the upper face which works corporately and complementarily with a time-shifted mouth to contribute the expression. Interestingly, the encoded features from the three sources are integrally balanced through reconstruction training. Extensive experiments show that our method generates expressive faces with not only synced mouth shapes, controllable poses, but precisely animated emotional expressions as well.

BACKGROUND: Quantitative areas is of great measurement of wound significance in clinical trials, wound pathological analysis, and daily patient care. 2D methods cannot solve the problems caused by human body curvatures and different camera shooting angles. Our objective is to simply collect wound areas, accurately measure wound areas and overcome the shortcomings of 2D methods. RESULTS: We propose a method with 3D transformation to measure wound area on a human body surface, which combines structure from motion (SFM), least squares conformal mapping (LSCM), and image segmentation. The method captures 2D images of wound, which is surrounded by adhesive tape scale next to it, by smartphone and implements 3D reconstruction from the images based on SFM. Then it uses LSCM to unwrap the UV map of the 3D model. In the end, it utilizes image segmentation by interactive method for wound extraction and measurement. Our system yields state-of-the-art results on a dataset of 118 wounds on 54 patients, and performs with an accuracy of 0.97. The Pearson correlation, standardized regression coefficient and adjusted R square of our method are 0.999, 0.895 and 0.998 respectively. CONCLUSIONS: A smartphone is used to capture wound images, which lowers costs, lessens dependence on hardware, and avoids the risk of infection. The quantitative calculation of the 3D wound area is realized, solving the challenges that 2D methods cannot and achieving a good accuracy.

Although many 3D head pose estimation methods based on monocular vision can achieve an accuracy of 5°, how to reduce the number of required training samples and how to not to use any hardware parameters as input features are still among the biggest challenges in the field of head pose estimation. To aim at these challenges, the authors propose an accurate head pose estimation method which can act as an extension to facial key point detection systems. The basic idea is to use the normalised distance between key points as input features, and to use ℓ 1 ‐minimisation to select a set of sparse training samples which can reflect the mapping relationship between the feature vector space and the head pose space. The linear combination of the head poses corresponding to these samples represents the head pose of the test sample. The experiment results show that the authors’ method can achieve an accuracy of 2.6° without any extra hardware parameters or information of the subject. In addition, in the case of large head movement and varying illumination, the authors’ method is still able to estimate the head pose.

Locating the center of the pupils is the most important foundation and the core component of gaze tracking. The accuracy of gaze tracking largely depends on the quality of images, but additional constraints and large amount of calculation make gaze tracking impractical on high-resolution images. Although some eye-gaze trackers can get accurate result, improving the accuracy of pupil feature on low-resolution images and accurately recognizing closed eye images are still common tasks in the field of gaze estimation. Our aim is to get the accurate localization of pupil center on low-resolution image. To this aim, we proposed a simple but effective method which can accurately locate pupil center in real time. The method first gets initial eye center based on improved scale-invariant feature transform (SIFT) descriptor and support vector machine (SVM) classifier, and then gets final position of the pupil center through a size variable correction rectangular block. In this paper, comparing with the reported state-of-the-art methods,the experimental results demonstrate that our system can achieve a more accurate result on low-resolution images. On top of that, our approach shows robustness on closed eye images while some other methods would not recognize the closed eye images.