4D Facial Expression Diffusion Model

Facial expression generation is one of the most challenging and long-sought aspects of character animation, with many interesting applications. The challenging task, traditionally having relied heavily on digital craftspersons, remains yet to be explored. In this paper, we introduce a generative framework for generating 3D facial expression sequences (i.e. 4D faces) that can be conditioned on different inputs to animate an arbitrary 3D face mesh. It is composed of two tasks: (1) Learning the generative model that is trained over a set of 3D landmark sequences, and (2) Generating 3D mesh sequences of an input facial mesh driven by the generated landmark sequences. The generative model is based on a Denoising Diffusion Probabilistic Model (DDPM), which has achieved remarkable success in generative tasks of other domains. While it can be trained unconditionally, its reverse process can still be conditioned by various condition signals. This allows us to efficiently develop several downstream tasks involving various conditional generation, by using expression labels, text, partial sequences, or simply a facial geometry. To obtain the full mesh deformation, we then develop a landmark-guided encoder-decoder to apply the geometrical deformation embedded in landmarks on a given facial mesh. Experiments show that our model has learned to generate realistic, quality expressions solely from the dataset of relatively small size, improving over the state-of-the-art methods. Videos and qualitative comparisons with other methods can be found at https://github.com/ZOUKaifeng/4DFM. Code and models will be made available upon acceptance

Multiobjective Memetic Estimation of Distribution Algorithm Based on an Incremental Tournament Local Searcher

A novel hybrid multiobjective algorithm is presented in this paper, which combines a new multiobjective estimation of distribution algorithm, an efficient local searcher and ε-dominance. Besides, two multiobjective problems with variable linkages strictly based on manifold distribution are proposed. The Pareto set to the continuous multiobjective optimization problems, in the decision space, is a piecewise low-dimensional continuous manifold. The regularity by the manifold features just build probability distribution model by globally statistical information from the population, yet, the efficiency of promising individuals is not well exploited, which is not beneficial to search and optimization process. Hereby, an incremental tournament local searcher is designed to exploit local information efficiently and accelerate convergence to the true Pareto-optimal front. Besides, since ε-dominance is a strategy that can make multiobjective algorithm gain well distributed solutions and has low computational complexity, ε-dominance and the incremental tournament local searcher are combined here. The novel memetic multiobjective estimation of distribution algorithm, MMEDA, was proposed accordingly. The algorithm is validated by experiment on twenty-two test problems with and without variable linkages of diverse complexities. Compared with three state-of-the-art multiobjective optimization algorithms, our algorithm achieves comparable results in terms of convergence and diversity metrics

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Avancées dans les modèles génératifs : amélioration de l'interprétabilité et du contrôle des données complexes grâce à la désentrelacement et la génération conditionnelle

Generative models are a class of machine learning models that aim to learn the underlying distribution of a given dataset and generate new data points that resemble the original data. These models have gained significant attention in recent years due to their ability to produce realistic and diverse samples of data. Generative models, such as VAEs ( Variational Autoencoders) , GANs (Generative Adversarial Networks), EBMs (Energy-Based Models), diffusion models, have shown significant promise in many fields, including image generation, speech synthesis, and natural language processing, and continue to be an active area of research, with new models and techniques being developed to improve their performance and broaden their applications. One of the most important application of generative model is disentangled representation, which refers to a type of feature learning in which the underlying factors or attributes of data are learned and represented independently. In our research, we utilize disentangled representations to tackle the challenge of sex determination and provide insights into the classification results. This is achieved by generating hip bones for the same individual from both sexes and subsequently conducting a comparison to identify sex-related distinctions. Additionally, we aim to acquire knowledge about the high-level factor and its attributes by learning the associated representation, allowing us to effectively control label-related characteristics. To achieve this, we introduce two innovative VAE frameworks aimed at learning the label-associated representation and enhancing VAE's generation quality simultaneously. Additionally, our research also makes a contribution to conditional generation. We apply a diffusion model to sequential data, showcasing its ability to generate 3D facial expressions, which involve time series data. This reverse process provides remarkable flexibility, enabling various types of conditioning and generation through a single training procedure.Les modèles génératifs sont une classe de modèles d'apprentissage automatique qui visent à apprendre la distribution sous-jacente d'un ensemble de données donné et à générer de nouveaux points de données qui ressemblent aux données originales. Ces modèles ont suscité beaucoup d'attention ces dernières années en raison de leur capacité à produire des échantillons de données réalistes et diversifiés. Les modèles génératifs, tels que les VAE (Variational Autoencoders), les GANs (Generative Adversarial Networks), les EBMs (Energy-Based Models), les modèles de diffusion, ont montré un grand potentiel dans de nombreux domaines, notamment la génération d'images, la synthèse de la parole et le traitement du langage naturel, et continuent d'être un domaine actif de recherche, avec de nouveaux modèles et techniques en développement pour améliorer leurs performances et élargir leurs applications. Une des applications les plus importantes des modèles génératifs est la représentation désentrelacée, qui fait référence à un type d'apprentissage des caractéristiques dans lequel les facteurs sous-jacents ou les attributs des données sont appris et représentés de manière indépendante. Dans notre recherche, nous utilisons des représentations désentrelacées pour relever le défi de la détermination du sexe et fournir des informations sur les résultats de classification. Cela est réalisé en générant des os de hanche pour le même individu des deux sexes, puis en effectuant une comparaison pour identifier les distinctions liées au sexe. De plus, nous visons à acquérir des connaissances sur le facteur de haut niveau et ses attributs en apprenant la représentation associée, ce qui nous permet de contrôler efficacement les caractéristiques liées à l'étiquette. Pour ce faire, nous introduisons deux cadres VAE innovants visant à apprendre la représentation associée à l'étiquette et à améliorer simultanément la qualité de la génération VAE. De plus, notre recherche contribue également à la génération conditionnelle. Nous appliquons un modèle de diffusion aux données séquentielles, montrant sa capacité à générer des expressions faciales 3D, impliquant des données en série temporelle. Ce processus inversé offre une flexibilité remarquable, permettant divers types de conditionnement et de génération grâce à une seule procédure de formation

Avancées dans les modèles génératifs : amélioration de l'interprétabilité et du contrôle des données complexes grâce à la désentrelacement et la génération conditionnelle

Les modèles génératifs sont une classe de modèles d'apprentissage automatique qui visent à apprendre la distribution sous-jacente d'un ensemble de données donné et à générer de nouveaux points de données qui ressemblent aux données originales. Ces modèles ont suscité beaucoup d'attention ces dernières années en raison de leur capacité à produire des échantillons de données réalistes et diversifiés. Les modèles génératifs, tels que les VAE (Variational Autoencoders), les GANs (Generative Adversarial Networks), les EBMs (Energy-Based Models), les modèles de diffusion, ont montré un grand potentiel dans de nombreux domaines, notamment la génération d'images, la synthèse de la parole et le traitement du langage naturel, et continuent d'être un domaine actif de recherche, avec de nouveaux modèles et techniques en développement pour améliorer leurs performances et élargir leurs applications. Une des applications les plus importantes des modèles génératifs est la représentation désentrelacée, qui fait référence à un type d'apprentissage des caractéristiques dans lequel les facteurs sous-jacents ou les attributs des données sont appris et représentés de manière indépendante. Dans notre recherche, nous utilisons des représentations désentrelacées pour relever le défi de la détermination du sexe et fournir des informations sur les résultats de classification. Cela est réalisé en générant des os de hanche pour le même individu des deux sexes, puis en effectuant une comparaison pour identifier les distinctions liées au sexe. De plus, nous visons à acquérir des connaissances sur le facteur de haut niveau et ses attributs en apprenant la représentation associée, ce qui nous permet de contrôler efficacement les caractéristiques liées à l'étiquette. Pour ce faire, nous introduisons deux cadres VAE innovants visant à apprendre la représentation associée à l'étiquette et à améliorer simultanément la qualité de la génération VAE. De plus, notre recherche contribue également à la génération conditionnelle. Nous appliquons un modèle de diffusion aux données séquentielles, montrant sa capacité à générer des expressions faciales 3D, impliquant des données en série temporelle. Ce processus inversé offre une flexibilité remarquable, permettant divers types de conditionnement et de génération grâce à une seule procédure de formation.Generative models are a class of machine learning models that aim to learn the underlying distribution of a given dataset and generate new data points that resemble the original data. These models have gained significant attention in recent years due to their ability to produce realistic and diverse samples of data. Generative models, such as VAEs ( Variational Autoencoders) , GANs (Generative Adversarial Networks), EBMs (Energy-Based Models), diffusion models, have shown significant promise in many fields, including image generation, speech synthesis, and natural language processing, and continue to be an active area of research, with new models and techniques being developed to improve their performance and broaden their applications. One of the most important application of generative model is disentangled representation, which refers to a type of feature learning in which the underlying factors or attributes of data are learned and represented independently. In our research, we utilize disentangled representations to tackle the challenge of sex determination and provide insights into the classification results. This is achieved by generating hip bones for the same individual from both sexes and subsequently conducting a comparison to identify sex-related distinctions. Additionally, we aim to acquire knowledge about the high-level factor and its attributes by learning the associated representation, allowing us to effectively control label-related characteristics. To achieve this, we introduce two innovative VAE frameworks aimed at learning the label-associated representation and enhancing VAE's generation quality simultaneously. Additionally, our research also makes a contribution to conditional generation. We apply a diffusion model to sequential data, showcasing its ability to generate 3D facial expressions, which involve time series data. This reverse process provides remarkable flexibility, enabling various types of conditioning and generation through a single training procedure

The Influence of New Agricultural Business Entities on the Economic Welfare of Farmer’s Families

Promoting the coordinated development of new agricultural business entities and small farmers is an important way to realize rural revitalization. It is undoubtedly of great significance to clarify the impact and its mechanism of new agricultural business entities on the economic welfare of farmers’ families. Based on the 2015 China Household Finance Survey (CHFS) data, this paper builds a theoretical analytical framework of “new agricultural business entities—non-agricultural employment and agricultural output—economic welfare of farmers’ family”. From the intermediary perspective of the non-agricultural employment and agricultural output, it empirically tests the impact of new agricultural business entities on the economic welfare of farmers’ families by combining the analysis methods of the benchmark regression and intermediary effect. The research shows that: (1) New agricultural business entities promote the improvement of the economic welfare of farmers’ families. The specific manifestation is that the existence of new agricultural business entities can not only increase the per capita annual income of farmers’ families, but also promote the per capita consumption expenditure of farmers’ families in the village. (2) Non-agricultural employment and agricultural output have a significant mediating effect in the impact of new agricultural business entities on the economic welfare of farmers’ families. (3) In addition to key variables, variables such as education, political status, and family status are also key factors affecting the economic welfare of farmers’ families. Finally, this paper puts forward some policy recommendations such as cultivating high-quality new agricultural business entities, strengthening farmers’ technical training, and optimizing rural residents’ policies

DSNet: Dynamic Skin Deformation Prediction by Recurrent Neural Network

International audienceSkin dynamics contributes to the enriched realism of human body models in rendered scenes. Traditional methods rely on physics-based simulations to accurately reproduce the dynamic behavior of soft tissues. Due to the model complexity and thus the heavy computation, however, they do not directly offer practical solutions to domains where real-time performance is desirable. The quality shapes obtained by physics-based simulations are not fully exploited by example-based or more recent datadriven methods neither, with most of them having focused on the modeling of static skin shapes by leveraging quality data. To address these limitations, we present a learningbased method for dynamic skin deformation. At the core of our work is a recurrent neural network that learns to predict the nonlinear, dynamics-dependent shape change over time from pre-existing mesh deformation sequence data. Our network also learns to predict the variation of skin dynamics across different individuals with varying body shapes. After training the network delivers realistic, high-quality skin dynamics that is specific to a person in a real-time course. We obtain results that significantly saves the computational time, while maintaining comparable prediction quality compared to state-of-the-art results

JOINT DISENTANGLEMENT OF LABELS AND THEIR FEATURES WITH VAE

International audienceMost of previous semi-supervised methods that seek to obtain disentangled representations using variational autoencoders divide the latent representation into two components: the non-interpretable part and the disentangled part that explicitly models the factors of interest. With such models, features associated with high-level factors are not explicitly modeled, and they can either be lost, or at best entangled in the other latent variables, thus leading to bad disentanglement properties. To address this problem, we propose a novel conditional dependency structure where both the labels and their features belong to the latent space. We show using the CelebA dataset that the proposed model can learn meaningful representations, and we provide quantitative and qualitative comparisons with other approaches that show the effectiveness of the proposed method

The Influence of New Agricultural Business Entities on Farmers’ Employment Decision

The purpose of this article is to explore the influence of new agricultural business entities on farmers’ employment decision and provide reference for improving policies related to new agricultural business entities and farmers’ employment. This paper constructs a theoretical analysis framework of “new agricultural business entities—land transfer and purchase of agricultural socialized services—farmers’ employment decision”, and then empirically tests the impact of new agricultural business entities on farmers’ employment decision by combining the analysis methods of the benchmark regression, propensity score matching and mediation effects. The research shows that: (1) New agricultural business entities are beneficial for promoting farmers’ employment decision. (2) Renting out land and the purchase of agricultural socialized services have a positive and partially mediating effect between the new agricultural business entities and farmers’ employment decision, and the mediating effects of the two paths account for 7.12% and 6.25% of the total effects, respectively. (3) In addition to key variables, personal characteristics of decision-making, family characteristics and production and management characteristics are also key factors that affect farmers’ employment decision. (4) The new agricultural business entities increase the probability of farmers’ employment (with legal contract) and entrepreneurship, and reduce the idle labor force in rural areas. Finally, this study proposes some policy recommendations including establishing a perfect farmland transfer market, developing rural industry properly and improving agricultural socialized service systems