Facial Animation Research Articles

Spatial Steerability of GANs via Self-Supervision from Discriminator.

Generative models make huge progress to the photorealistic image synthesis in recent years. To enable humans to steer the image generation process and customize the output, many works explore the interpretable dimensions of the latent space in GANs. Existing methods edit the attributes of the output image such as orientation or color scheme by varying the latent code along certain directions. However, these methods usually require additional human annotations for each pretrained model, and they mostly focus on editing global attributes. In this work, we propose a self-supervised approach to improve the spatial steerability of GANs without searching for steerable directions in the latent space or requiring extra annotations. Specifically, we design randomly sampled Gaussian heatmaps to be encoded into the intermediate layers of generative models as spatial inductive bias. Along with training the GAN model from scratch, these heatmaps are aligned with the emerging attention of the GAN's discriminator in a self-supervised learning manner. During inference, users can interact with the spatial heatmaps in an intuitive manner, enabling them to edit the output image by adjusting the scene layout, moving, or removing objects. Moreover, we incorporate DragGAN into our framework, which facilitates fine-grained manipulation within a reasonable time and supports a coarse-to-fine editing process. Extensive experiments show that the proposed method not only enables spatial editing over human faces, animal faces, outdoor scenes, and complicated multi-object indoor scenes but also brings improvement in synthesis quality.

A Review of Facial Generation Technology Research

Abstract. Face generation, as a cutting-edge generative technology, has made significant strides in various image synthesis tasks, including facial inpainting, text-to-image translation, and video-based facial animation. Generating realistic and diverse human faces is a critical task in computer vision, with a wide array of real-world applications. Given the remarkable success of face generation models, there has been a growing interest in leveraging advanced techniques to further improve the quality, diversity, and control of generated faces. This paper will provide a comprehensive overview of the state-of-the-art face generation techniques. Specifically, the paper reviews the key approaches in this field, including Generative Adversarial Networks (GANs), Vector Quantized methods, and the more recent Diffusion Models, each of which has contributed significantly to the advancement of face generation. The paper discusses how these models have evolved to handle the complexity of face generation, from capturing subtle facial details to enabling fine-grained control over facial attributes. The paper also explores the major applications of face generation technologies, with a particular emphasis on their use in entertainment, virtual reality, and security. Finally, the paper identifies promising directions for future research in face generation, such as improving the interpretability of models, addressing ethical concerns, and enhancing the ability to generate faces that are both highly realistic and diverse.

Expressive 3D Facial Animation Generation Based on Local-to-Global Latent Diffusion.

3D Facial animations, crucial to augmented and mixed reality digital media, have evolved from mere aesthetic elements to potent storytelling media. Despite considerable progress in facial animation of neutral emotions, existing methods still struggle to capture the authenticity of emotions. This paper introduces a novel approach to capture fine facial expressions and generate facial animations using audio synchronization. Our method consists of two key components: First, the Local-to-global Latent Diffusion Model (LG-LDM) tailored for authentic facial expressions, which can integrate audio, time step, facial expressions, and other conditions towards possible encoding of emotionally rich yet latent features in response to possibly noisy raw audio signals. The core of LG-LDM is our carefully designed Facial Denoiser Model (FDM) for aligning the local-to-global animation feature with audio. Second, we redesign an Emotion-centric Vector Quantized-Variational AutoEncoder framework (EVQ-VAE) to finely decode the subtle differences under different emotions and reconstruct the final 3D facial geometry. Our work significantly contributes to the key challenges of emotionally realistic 3D facial animation for audio synchronization and enhances the immersive experience and emotional depth in augmented and mixed reality applications. We provide a reproducibility kit including our code, dataset, and detailed instructions for running the experiments. This kit is available at https://github.com/wangxuanx/Face-Diffusion-Model.

Toll-like receptor signaling in neurons modulates C. elegans feeding behavior in a hunger state-dependent manner

Animals face the risk of encountering pathogenic microbes while foraging for resources. Assessing the risk of nutrition vs. infection can result in the behavioral regulation of immune processes. Behavioral immunity in the nematode roundworm Caenorhabditis elegans (C. elegans) is regulated, in part, by the innate immune molecule TOL-1: a homolog of vertebrate Toll-like Receptor (TLR) proteins that influences C. elegans pathogen avoidance behaviors by promoting the development of CO2-detecting chemosensory neurons. While TOL-1′s role in pathogen avoidance is well established, its role in an opposing behavior – foraging – has not been examined. In addition to pathogenic bacteria, preferred food for C. elegans, such as Escherichia coli (E. coli), create significant and aversive environmental CO2 levels which may limit feeding behaviors in a tol-1 dependent manner. We have found that in addition to conferring antibacterial immunity, TOL-1 signals in neurons through the p38 MAPK PMK-1 to promote turning behavior and limit foraging when food is abundant and that the anorectic TOL-1/PMK-1 pathway is attenuated during starvation to promote foraging These data highlight the dynamic role of a conserved innate immune cascade in neurons during both high and low hunger states and identify mechanisms underlying the neuro-immune control of feeding strategies.

Full life cycle assessment of insect biomass allows estimation of bioflows across water, air, and land

AbstractAs global environmental change continues, animals face uncertain habitat availability and quality that influences life cycle phenology and population dynamics. For decades, the population abundance and emergence patterns of burrowing mayflies have been used as a sentinel for water quality changes in large freshwater systems around the world. Despite reduced point source pollutants, evidence is mounting that the interactions among habitat loss, contaminants, and changing climate could be causing declines in mayfly production and shifts in emergence timing. We combined radar observations with traditional field measures to identify changes in mayfly populations from nymph to adult. We studied Hexagenia sp. secondary production in a large reservoir, Lake Seminole, which has contrasting water sources and land use on each arm that could contribute to differences in emergence patterns. We predicted that mayfly secondary production would be higher, and emergence would be earlier in the Chattahoochee arm versus the Flint arm because of differences in available nutrients and temperature. Benthic declines in abundance and biomass followed radar observations of emergence. Mean annual water temperature was similar, with the Flint arm having less seasonal variation. Mayfly growth was similar across the lake, but production was higher in the upper Flint arm, perhaps because of temperature stability, higher nutrient concentrations, and more lotic conditions. The natural abundance of nitrogen‐stable isotopes in mayflies showed distinct patterns between the arms and from nymph to adult. Linking benthic sampling with radar observations verified our capability to track mayfly biomass across the landscape and begin to calibrate previous measures of production with radar‐derived abundance. Coupling radar observations with stable isotope and tissue nutrient measurements allowed us to further quantify the subsidies moving from aquatic to terrestrial ecosystems, setting the framework to examine both historic and future population changes and mayfly contributions to cross‐ecosystem subsidies.

Audio-Driven Facial Animation with Deep Learning: A Survey

Audio-driven facial animation is a rapidly evolving field that aims to generate realistic facial expressions and lip movements synchronized with a given audio input. This survey provides a comprehensive review of deep learning techniques applied to audio-driven facial animation, with a focus on both audio-driven facial image animation and audio-driven facial mesh animation. These approaches employ deep learning to map audio inputs directly onto 3D facial meshes or 2D images, enabling the creation of highly realistic and synchronized animations. This survey also explores evaluation metrics, available datasets, and the challenges that remain, such as disentangling lip synchronization and emotions, generalization across speakers, and dataset limitations. Lastly, we discuss future directions, including multi-modal integration, personalized models, and facial attribute modification in animations, all of which are critical for the continued development and application of this technology.

Deep‐Learning‐Based Facial Retargeting Using Local Patches

AbstractIn the era of digital animation, the quest to produce lifelike facial animations for virtual characters has led to the development of various retargeting methods. While the retargeting facial motion between models of similar shapes has been very successful, challenges arise when the retargeting is performed on stylized or exaggerated 3D characters that deviate significantly from human facial structures. In this scenario, it is important to consider the target character's facial structure and possible range of motion to preserve the semantics assumed by the original facial motions after the retargeting. To achieve this, we propose a local patch‐based retargeting method that transfers facial animations captured in a source performance video to a target stylized 3D character. Our method consists of three modules. The Automatic Patch Extraction Module extracts local patches from the source video frame. These patches are processed through the Reenactment Module to generate correspondingly re‐enacted target local patches. The Weight Estimation Module calculates the animation parameters for the target character at every frame for the creation of a complete facial animation sequence. Extensive experiments demonstrate that our method can successfully transfer the semantic meaning of source facial expressions to stylized characters with considerable variations in facial feature proportion.

Quality Determination of Dairy Farm Wastewater in Dinajpur

At present environmental pollution is a talked about issue. Due to environmental pollution, humans and animals face threats. The scientist has pointed out that waste is one reason for climate change. Solid, liquid, gaseous etc. are different types of waste. The experiments were conducted to determine the chemical constituents present in dairy farm wastewater, wastewater management practice and environmental impact and compare it with the groundwater Dinajpur Sadar upazila. The data concerning the dairy farm wastewater in Dinajpur was obtained through a designed questionnaire, and separate area inspection interacting with the proprietor and workers in the dairy farm. Randomly collected samples from different dairy farms. The chemical constituents of the wastewater sample were determined by a laboratory experiment. The wastewater contained Mg, Na, Ca, Cl-, K, EC, P, HCO3-, pH, TDS, DO, COD, Zn, S, BOD, HT and NO3-. The Na, K, Ca and pH concentration of wastewater under the range in groundwater in Dinajpur. However, concentrations of P, TDS, Mg, EC, Cl-, HCO3- and HT are above the groundwater range in Dinajpur. The produced wastewater was disposed of either through drainage or piping systems on fellow land, ponds, open lakes, roadside land, urban drains, and rivers. The unplanned wastewater disposal creates bad odor, and environmental pollution, seduces the growth of mosquitoes decreases the water quality, soil quality and health hazards. Therefore, it can be concluded that the prevailing dairy farm wastewater disposal system not being satisfactory. The proper disposal system should be improved to reduce environmental impacts.

Ketamine modulates the exploratory dynamics and homebase-related behaviors of adult zebrafish

Anxiety can be a protective emotion when animals face aversive conditions, but is commonly associated with various neuropsychiatric disorders when pathologically exacerbated. Drug repurposing has emerged as a valuable strategy based on utilizing the existing pharmaceuticals for new therapeutic purposes. Ketamine, traditionally used as an anesthetic, acts as a non-competitive antagonist of the glutamate N-methyl-d-aspartate (NMDA) receptor, and shows potential anxiolytic and antidepressant effects at subanesthetic doses. However, the influence of ketamine on multiple behavioral domains in vertebrates is not completely understood. Here, we evaluated the potential modulatory effect of ketamine on the spatio-temporal exploratory dynamics and homebase-related behaviors in adult zebrafish using the open field test (OFT). Animals were exposed to subanesthetic concentrations of ketamine (0, 2, 20, and 40 mg/L) for 20 min and their locomotion-, exploration- and homebase-related behaviors were assessed in a single 30-min trial. Our data revealed that acute ketamine (20 and 40 mg/L) induced hyperlocomotion, as verified by the increased total distance traveled. All concentrations tested elicited circling behavior, a stereotyped-like response which gradually reduced across the periods of test. We also observed modulatory effects of ketamine on the spatio-temporal exploratory pattern, in which the reduced thigmotaxis and homebase activity, associated with the increased average length of trips, suggest anxiolytic-like effects. Collectively, our findings support the modulatory effects of ketamine on the spatio-temporal exploratory activity, and corroborate the utility of homebase-related measurements to evaluate the behavioral dynamics in zebrafish models.

Modulation of face processing by top-down attention: Insights from early ERP waveforms

The face fusiform area (FFA) plays a pivotal role in face recognition, yet the precise timeline of its activity remains debated. Using EEG, we conducted three experiments to investigate how expectancy-consistent versus expectancy-inconsistent visual stimuli influence processing dynamics. Participants viewed images of faces, houses, and tools (Experiment 1), celebrity faces (Experiment 2), or animal faces (Experiment 3), preceded by a priming question. Notably, both conditions presented identical visual stimulation, ensuring that observed differences stemmed from cognitive processing rather than sensory input. Our results from Experiments 2 and 3 reveal that while the initial 150 ms period, crucial for unconscious face detection, remained unaffected, subsequent processing exhibited a delay of several milliseconds for expectancy-inconsistent stimuli, indicating additional processing time required for unexpected recognition. Importantly, no significant differences were observed in Experiment 1, where less demanding tasks or generic mental imagery were used, suggesting that the priming effect was not as pronounced in this context. These findings underscore the critical role of the period immediately following the first 150 ms in face identification and individuation, highlighting the influence of top-down attention on face recognition dynamics. This study provides novel insights into the temporal dynamics of face processing and the neural mechanisms underlying top-down attentional modulation.

High-definition multi-scale voice-driven facial animation: enhancing lip-sync clarity and image detail

High-definition multi-scale voice-driven facial animation: enhancing lip-sync clarity and image detail

AnyFace++: Deep Multi-Task, Multi-Domain Learning for Efficient Face AI.

Accurate face detection and subsequent localization of facial landmarks are mandatory steps in many computer vision applications, such as emotion recognition, age estimation, and gender identification. Thanks to advancements in deep learning, numerous facial applications have been developed for human faces. However, most have to employ multiple models to accomplish several tasks simultaneously. As a result, they require more memory usage and increased inference time. Also, less attention is paid to other domains, such as animals and cartoon characters. To address these challenges, we propose an input-agnostic face model, AnyFace++, to perform multiple face-related tasks concurrently. The tasks are face detection and prediction of facial landmarks for human, animal, and cartoon faces, including age estimation, gender classification, and emotion recognition for human faces. We trained the model using deep multi-task, multi-domain learning with a heterogeneous cost function. The experimental results demonstrate that AnyFace++ generates outcomes comparable to cutting-edge models designed for specific domains.

VividWav2Lip: High-Fidelity Facial Animation Generation Based on Speech-Driven Lip Synchronization

Speech-driven lip synchronization is a crucial technology for generating realistic facial animations, with broad application prospects in virtual reality, education, training, and other fields. However, existing methods still face challenges in generating high-fidelity facial animations, particularly in addressing lip jitter and facial motion instability issues in continuous frame sequences. This study presents VividWav2Lip, an improved speech-driven lip synchronization model. Our model incorporates three key innovations: a cross-attention mechanism for enhanced audio-visual feature fusion, an optimized network structure with Squeeze-and-Excitation (SE) residual blocks, and the integration of the CodeFormer facial restoration network for post-processing. Extensive experiments were conducted on a diverse dataset comprising multiple languages and facial types. Quantitative evaluations demonstrate that VividWav2Lip outperforms the baseline Wav2Lip model by 5% in lip sync accuracy and image generation quality, with even more significant improvements over other mainstream methods. In subjective assessments, 85% of participants perceived VividWav2Lip-generated animations as more realistic compared to those produced by existing techniques. Additional experiments reveal our model’s robust cross-lingual performance, maintaining consistent quality even for languages not included in the training set. This study not only advances the theoretical foundations of audio-driven lip synchronization but also offers a practical solution for high-fidelity, multilingual dynamic face generation, with potential applications spanning virtual assistants, video dubbing, and personalized content creation.

2D/3D Expression Generation Using Advanced Learning Techniques and the Emotion Wheel

Facial expression analysis is a critical component in numerous applications, ranging from human- computer interaction to digital character animation. Despite the availability of extensive datasets, most focus predominantly on basic emotions, limiting the expressiveness and applicability of models trained on them. This article introduces a novel approach to generating compound emotional expressions by leveraging the Emotion Wheel, a principle that captures the complex interrelations between basic emotions. Our method integrates the EMOCA (Emotion Driven Monocular Face Capture and Animation) [1] framework, which enhances 3D facial reconstruction by incorporating emotion recognition, stacking models [2, 54] with a sophisticated expression blending algorithm to synthesize nuanced 2D and 3D facial animations. Utilizing the VKIST dataset, which includes high-resolution facial images of Vietnamese individuals, we build a comprehensive database of emotion parameters. Through principal component analysis (PCA) and correlation-driven blending, our approach not only enhances the realism of generated facial expressions but also preserves the subtle nuances that are characteristic of human emotions. Experimental results demonstrate that our method consistently outperforms traditional linear interpolation techniques, producing more distinct and recognizable blended expressions. A user study further validates the naturalness and quality of the generated expressions, with average ratings indicating a strong preference for the proposed method over existing approaches. These findings suggest significant potential for improving emotional expressiveness in both static and dynamic digital character representations.

Lifting With Neuromodulators.

The use of botulinum toxins for facial rejuvenation and improvement of dynamic wrinkles has become a mainstay in the aesthetic treatment armamentarium. However, using these same neuromodulators (NMs) for lifting the structures of the face, is a newer addition to antiaging protocols. The muscles of facial animation all interplay with each other. Lifting can be accomplished by treating those muscles that are responsible for depression, leaving the elevators unopposed and resulting in a rejuvenated, lifted outcome. Brow lifting, cheek lifting, and even contouring of the lower face and jawline are all possibilities using NMs. To review the literature and current practices in techniques for lifting the different anatomic facial zones. The authors present and discuss the published data and personal experiences of using NM for lifting and retraining of the facial musculature. This article will discuss the effects and approaches to lifting with botulinum toxin injections, including the potential success and side effects associated with these off-label injections. The use of botulinum toxins has expanded beyond its traditional use as a reducer of dynamic wrinkles. A significant amount of published data now exists for the off-label use of botulinum toxins for lifting and shaping the face. These can be considered advanced techniques as each region has its own anatomic intricacies and side effects can occur. More placebo-controlled objective data would also help elucidate exact dosing strategies for each region.

Molecular analysis of feces reveals gastrointestinal nematodes in reintroduced wild asses of the Negev desert

Reintroduced animals face disease risks, potentially impacting both the reintroduced and the local wildlife/domestic populations. This study focuses on the Asiatic wild asses (Equus hemionus) reintroduced to the Negev desert in southern Israel. Despite potential threats of disease spill-over to and from domesticated donkeys and horses in the area, there are no records of the gastrointestinal nematodes (GIN) of the wild ass population. We used DNA metabarcoding on fecal samples of wild asses collected across seasons and habitats, near water sources that they frequently use. Ten GIN species were detected in the feces, nine belonging to the family Strongylidae, which commonly infects and causes disease in equids worldwide, such as horses, zebras, and donkeys. Some of these Strongylidae species are also found in domesticated equids in Israel, thus raising concerns regarding potential parasite transmission between wild and domestic animals. The high prevalence of certain GIN species suggests frequent transmission, likely due to the congregation of the wild asses around water sources. While we observed statistically significant variations in some GIN species across seasons and habitats, we did not find clear overall differences between GIN communities. DNA metabarcoding proves to be a valuable tool for identifying GIN species in wild animals, with potential applications in monitoring their health and preventing disease transmission to and from domestic animals.

The Role of Botulinum Toxin A Neuromodulator in the Management of Synkinesis in Facial Palsy.

Facial palsy describes the denervation of the facial nerve leading to difficulty in facial animation and expression. Facial synkinesis is the result of complex pathological nerve regeneration following damage to the facial nerve axons. Synkinesis in facial palsy can be managed using facial neuromuscular rehabilitation, botulinum toxin neuromodulators, and surgical treatment options. Botulinum toxin A can be used as an adjunct to other treatment options to manage synkinesis. This article will explore the role of botulinum toxin A in the management of synkinesis in facial palsy including the clinical assessment, injection location (muscles targeted), dosages, treatment interval, and long-term results. It will also include surgical management options.

Automatic Face Detection of Farm Images Based on an Enhanced Lightweight Deep Learning Model

In the realm of precise management, artificial intelligence has garnered significant attention and adoption, particularly within the domain of smart agriculture. In modern animal husbandry, animal face detection is conducive to individual identification, expression detection and behavior analysis of animals, and this technological advancement holds immense importance in fostering the advancement of intelligent farming practices. In order to solve the challenge of face detection caused by similar appearance features (color, texture, etc.) and no obvious feature differences between the solid-color goats and sheep in the natural environment, this research introduces a novel approach for face detection by combining the capabilities of YOLOv5 and a convolutional block attention module (CBAM). First, datasets of goats and sheep with different angles, scales and densities were constructed. Second, the basic framework of YOLOv5 was used for object detection. To address the obstacle posed by the limited presence of distinguishing features on the faces of goats and sheep, this study aims to overcome the challenge of extracting informative facial characteristics. The CBAM block was introduced to construct the YOLOv5-CBAM model to improve the feature extraction ability. Finally, 2412 images were selected and divided into training set and verification set according to 8:1. The experimental results of this dataset show that the proposed YOLOv5-CBAM model yielded remarkable results with a precision rate of 0.970, a recall rate of 0.890, a mAP@0.5 score of 0.935, an frames per second (FPS) of 140.845, and a model size of 14.680[Formula: see text]MB. In comparison to other approaches such as Faster R-CNN, SSD, YOLOv3, and YOLOv5, the proposed model demonstrated superior performance in some aspects. In addition, it excelled in both lightweight design and overall effectiveness, and it is well-suited for real-time detection of animal faces in real-world farming settings, ensuring efficient identification and monitoring of animals within practical agricultural environments.

Dissemination and Interpretation of Animated Films in Intercultural Communication in China

As an important carrier for displaying local culture and national image, animated films play a key role in inheriting and innovating Chinese culture and cultivating national self-consciousness and cultural confidence. However, in the process of cross-cultural communication, Chinese animated films face problems such as an imperfect communication mechanism, insufficient cultural adaptability, and limited international influence.By analyzing these problems, this paper explores the specific communication mechanism and path of animated films in cross-cultural communication. It is found that there are problems such as improper balance between localization and internationalization of cultural content, lack of marketing strategy, and single communication channel in cross-cultural communication of Chinese animated films.Through case studies and empirical research, this paper proposes a series of strategies to optimize the dissemination mechanism, including enhancing the international adaptability of cultural content, adopting diversified dissemination channels, and strengthening marketing and branding in order to enhance the international competitiveness and cultural influence of Chinese animated films.The research in this paper provides concrete practical guidance and theoretical support for better dissemination and development of Chinese animated films in the context of globalization.

Micro and macro facial expressions by driven animations in realistic Virtual Humans

Computer Graphics (CG) advancements have allowed the creation of more realistic Virtual Humans (VH) through modern techniques for animating the VH body and face, thereby affecting perception. From traditional methods, including blend shapes, to driven animations using facial and body tracking, these advancements can potentially enhance the perception of comfort and realism in relation to VHs. Previously, Psychology studied facial movements in humans, with some works separating expressions into macro and micro expressions. Also, some previous CG studies have analyzed how macro and micro expressions are perceived, replicating psychology studies in VHs, encompassing studies with realistic and cartoon VHs, and exploring different VH technologies. However, instead of using facial tracking animation methods, these previous studies animated the VHs using blendshapes interpolation. To understand how the facial tracking technique alters the perception of VHs, this paper extends the study to macro and micro expressions, employing two datasets to transfer real facial expressions to VHs and analyze how their expressions are perceived. Our findings suggest that transferring facial expressions from real actors to VHs significantly diminishes the accuracy of emotion perception compared to VH facial animations created by artists.