CGOF++: Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields

Capitalizing on the recent advances in image generation models, existing controllable face image synthesis methods are able to generate high-fidelity images with some levels of controllability, e.g., controlling the shapes, expressions, textures, and poses of the generated face images. However, previous methods focus on controllable 2D image generative models, which are prone to producing inconsistent face images under large expression and pose changes. In this paper, we propose a new NeRF-based conditional 3D face synthesis framework, which enables 3D controllability over the generated face images by imposing explicit 3D conditions from 3D face priors. At its core is a conditional Generative Occupancy Field (cGOF++) that effectively enforces the shape of the generated face to conform to a given 3D Morphable Model (3DMM) mesh, built on top of EG3D [1], a recent tri-plane-based generative model. To achieve accurate control over fine-grained 3D face shapes of the synthesized images, we additionally incorporate a 3D landmark loss as well as a volume warping loss into our synthesis framework. Experiments validate the effectiveness of the proposed method, which is able to generate high-fidelity face images and shows more precise 3D controllability than state-of-the-art 2D-based controllable face synthesis methods.Comment: This article is an extension of the NeurIPS'22 paper arXiv:2206.0836

Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields

Capitalizing on the recent advances in image generation models, existing controllable face image synthesis methods are able to generate high-fidelity images with some levels of controllability, e.g., controlling the shapes, expressions, textures, and poses of the generated face images. However, these methods focus on 2D image generative models, which are prone to producing inconsistent face images under large expression and pose changes. In this paper, we propose a new NeRF-based conditional 3D face synthesis framework, which enables 3D controllability over the generated face images by imposing explicit 3D conditions from 3D face priors. At its core is a conditional Generative Occupancy Field (cGOF) that effectively enforces the shape of the generated face to commit to a given 3D Morphable Model (3DMM) mesh. To achieve accurate control over fine-grained 3D face shapes of the synthesized image, we additionally incorporate a 3D landmark loss as well as a volume warping loss into our synthesis algorithm. Experiments validate the effectiveness of the proposed method, which is able to generate high-fidelity face images and shows more precise 3D controllability than state-of-the-art 2D-based controllable face synthesis methods. Find code and demo at https://keqiangsun.github.io/projects/cgof

Left bundle branch pacing in third-degree atrioventricular block following morrow surgery: a case report

Left bundle branch pacing (LBBP) has proven to be an alternative method for delivering physiological pacing to achieve electrical synchrony of the left ventricle (LV), especially in patients with atrioventricular block and left bundle branch block (LBBB). However, it is unclear whether it still achieved in patients whose left bundle branch (LBB) has had surgery-induced damage. The Morrow operation (Morrow septal myectomy) is regarded as one of the most effective treatments for hypertrophic obstructive cardiomyopathy (HOCM). The surgery resects small sections of muscle tissue in the proximal ventricular septum nearby or contains the LBB, which means that physical damage to the LBB is almost inevitable. Approximately 2%–12% of patients may need pacemaker implanted after Morrow surgery. LBBP is a feasible and effective method for achieving electric resynchronization of LBBB compared to right ventricular pacing (RVB). Nevertheless, there is a dearth of data on LBBP in third-degree atrioventricular block (AVB) following Morrow surgery. We report a case of successful LBBP in those patients

Fat and Moisture Content in Chinese Fried Bread Sticks: Assessment and Rapid Near-Infrared Spectroscopic Method Development

Fried bread sticks (FBS) are one of the most widely consumed deep fried food products in China. Understanding the fat and moisture content in FBS will help consumers make healthy food choices as well as assist food processors to provide FBS with desirable quality. Rapid Fourier transform near-infrared methods (FT-NIR) were developed for determining fat and moisture content in FBS collected from 123 different vendors in Shanghai, China. FBS samples with minimum sample preparation (either finely or coarsely ground) were used for NIR analyses. Spectra of FBS were treated with different mathematic pretreatments before being used to build models between the spectral information and fat (7.71%–30.89%) or moisture (17.39%–32.65%) content in FBS. Finely ground samples may lead to slightly more robust PLS models, but the particle sizes of ground FBS samples did not seriously affect the predictability of the models with appropriate mathematical treatments. The fat and moisture content in FBS predicted by FT-NIR methods had very good correlation with their values determined via traditional methods (fat, R2=0.965; moisture, R2=0.983), which clearly indicated that FT-NIR methods could be used as an effective tool for rapid determination of fat and moisture content in FBS