Recently, a technique named improved integration approximation (IIA) has been introduced in [1], aiming to enhance the performance of the EDM [2] sampling procedure for small timesteps. The basic idea of IIA-EDM is to optimize the step-sizes in front of the gradient vectors per timestep when computing the next diffusion state via minimum mean squared error (MMSE). In this paper, we further improve the sampling performance of IIA-EDM by optimizing the timesteps. Our main contribution is to utilize the residual errors of the MMSE in IIA-EDM as the objective function and then apply the genetic algorithm to minimize the residual error w.r.t. the timesteps. We refer to the resulting sampling procedure as OTS-IIA-EDM, where OTS stands for optimized timesteps. With the OTS-IIA-EDM, we obtain substantial performance enhancements on different datasets (CIFAR-10, FFHQ, AFHQv2, ImageNet) in comparison to both IIA-EDM and EDM. Notably, when utilizing 6 timesteps, OTS-IIA-EDM demonstrates a noteworthy FID improvement, ranging from 10.8 to 18.4 percent compared to IIA-EDM, and a more substantial boost of 40.1 to 69.8 percent to EDM.

Synthesizing high-fidelity head avatars is a central problem for computer vision and graphics. While head avatar synthesis algorithms have advanced rapidly, the best ones still face great obstacles in real-world scenarios. One of the vital causes is the inadequate datasets -- 1) current public datasets can only support researchers to explore high-fidelity head avatars in one or two task directions; 2) these datasets usually contain digital head assets with limited data volume, and narrow distribution over different attributes, such as expressions, ages, and accessories. In this paper, we present RenderMe-360, a comprehensive 4D human head dataset to drive advance in head avatar algorithms across different scenarios. It contains massive data assets, with 243+ million complete head frames and over 800k video sequences from 500 different identities captured by multi-view cameras at 30 FPS. It is a large-scale digital library for head avatars with three key attributes: 1) High Fidelity: all subjects are captured in 360 degrees via 60 synchronized, high-resolution 2K cameras. 2) High Diversity: The collected subjects vary from different ages, eras, ethnicities, and cultures, providing abundant materials with distinctive styles in appearance and geometry. Moreover, each subject is asked to perform various dynamic motions, such as expressions and head rotations, which further extend the richness of assets. 3) Rich Annotations: the dataset provides annotations with different granularities: cameras' parameters, background matting, scan, 2D/3D facial landmarks, FLAME fitting, and text description. Based on the dataset, we build a comprehensive benchmark for head avatar research, with 16 state-of-the-art methods performed on five main tasks: novel view synthesis, novel expression synthesis, hair rendering, hair editing, and talking head generation. Our experiments uncover the strengths and flaws of state-of-the-art methods. RenderMe-360 opens the door for future exploration in modern head avatars. All of the data, code, and models will be publicly available at https://renderme-360.github.io/.

We introduce a highly robust GAN-based framework for digitizing a normalized 3D avatar of a person from a single unconstrained photo. While the input image can be of a smiling person or taken in extreme lighting conditions, our method can reliably produce a high-quality textured model of a person’s face in neutral expression and skin textures under diffuse lighting condition. Cutting-edge 3D face reconstruction methods use non-linear morphable face models combined with GAN-based decoders to capture the likeness and details of a person but fail to produce neutral head models with unshaded albedo textures which is critical for creating relightable and animation-friendly avatars for integration in virtual environments. The key challenges for existing methods to work is the lack of training and ground truth data containing normalized 3D faces. We propose a two-stage approach to address this problem. First, we adopt a highly robust normalized 3D face generator by embedding a non-linear morphable face model into a StyleGAN2 network. This allows us to generate detailed but normalized facial assets. This inference is then followed by a perceptual refinement step that uses the generated assets as regularization to cope with the limited available training samples of normalized faces. We further introduce a Normalized Face Dataset, which consists of a combination photogrammetry scans, carefully selected photographs, and generated fake people with neutral expressions in diffuse lighting conditions. While our prepared dataset contains two orders of magnitude less subjects than cutting edge GAN-based 3D facial reconstruction methods, we show that it is possible to produce high-quality normalized face models for very challenging unconstrained input images, and demonstrate superior performance to the current state-of-the-art.

From angling smiles to duck faces, all kinds of facial expressions can be seen in selfies, portraits, and Internet pictures. These photos are taken from various camera types, and under a vast range of angles and lighting conditions. We present a deep learning framework that can fully normalize unconstrained face images, i.e., remove perspective distortions, relight to an evenly lit environment, and predict a frontal and neutral face. Our method can produce a high resolution image while preserving important facial details and the likeness of the subject, along with the original background. We divide this ill-posed problem into three consecutive normalization steps, each using a different generative adversarial network that acts as an image generator. Perspective distortion removal is performed using a dense flow field predictor. A uniformly illuminated face is obtained using a lighting translation network, and the facial expression is neutralized using a generalized facial expression synthesis framework combined with a regression network based on deep features for facial recognition. We introduce new data representations for conditional inference, as well as training methods for supervised learning to ensure that different expressions of the same person can yield to not only a plausible but also a similar neutral face. We demonstrate our results on a wide range of challenging images collected in the wild. Key applications of our method range from robust image-based 3D avatar creation, portrait manipulation, to facial enhancement and reconstruction tasks for crime investigation. We also found through an extensive user study, that our normalization results can be hardly distinguished from ground truth ones if the person is not familiar.