IEEE Transactions on Pattern Analysis and Machine Intelligence

A morphable model for the synthesis of 3D faces

We advocate the use of implicit fields for learning generative models of shapes and introduce an implicit field decoder, called IM-NET, for shape generation, aimed at improving the visual quality of the generated shapes. An implicit field assigns a value to each point in 3D space, so that a shape can be extracted as an iso-surface. IM-NET is trained to perform this assignment by means of a binary classifier. Specifically, it takes a point coordinate, along with a feature vector encoding a shape, and outputs a value which indicates whether the point is outside the shape or not. By replacing conventional decoders by our implicit decoder for representation learning (via IM-AE) and shape generation (via IM-GAN), we demonstrate superior results for tasks such as generative shape modeling, interpolation, and single-view 3D reconstruction, particularly in terms of visual quality. Code and supplementary material are available at https://github.com/czq142857/implicit-decoder.

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Learning Implicit Fields for Generative Shape Modeling

Point cloud learning has lately attracted increasing attention due to its wide applications in many areas, such as computer vision, autonomous driving, and robotics As a dominating technique in AI, deep learning has been successfully used to solve various 2D vision problems However, deep learning on point clouds is still in its infancy due to the unique challenges faced by the processing of point clouds with deep neural networks Recently, deep learning on point clouds has become even thriving, with numerous methods being proposed to address different problems in this area To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds It covers three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation It also presents comparative results on several publicly available datasets, together with insightful observations and inspiring future research directions

Deep Learning for 3D Point Clouds: A Survey

We introduce Pixel-aligned Implicit Function (PIFu), an implicit representation that locally aligns pixels of 2D images with the global context of their corresponding 3D object. Using PIFu, we propose an end-to-end deep learning method for digitizing highly detailed clothed humans that can infer both 3D surface and texture from a single image, and optionally, multiple input images. Highly intricate shapes, such as hairstyles, clothing, as well as their variations and deformations can be digitized in a unified way. Compared to existing representations used for 3D deep learning, PIFu produces high-resolution surfaces including largely unseen regions such as the back of a person. In particular, it is memory efficient unlike the voxel representation, can handle arbitrary topology, and the resulting surface is spatially aligned with the input image. Furthermore, while previous techniques are designed to process either a single image or multiple views, PIFu extends naturally to arbitrary number of views. We demonstrate high-resolution and robust reconstructions on real world images from the DeepFashion dataset, which contains a variety of challenging clothing types. Our method achieves state-of-the-art performance on a public benchmark and outperforms the prior work for clothed human digitization from a single image.

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PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization

Iris segmentation is an important module of iris recognition that can substantially affect recognition performance. Since iris and pupil boundaries usually are not exactly circular, spline-based methods have been used to model irregular iris and pupil boundaries recently. However, in most existing methods, many other factors or modules in the iris recognition pipeline are evaluated together and their mixed effects are assumed to be negligible. More importantly, the splines that model irregularity of the boundaries could not be enough to model the internal nonlinear deformations of an iris pattern (e.g., caused by iris dilation). As a result, it remains unclear whether spline-based methods can provide significant improvements. In this paper, we conduct a complete performance comparison between circular and spline-based methods. There are mainly two contributions. Firstly, for the purpose of comparison, we propose a spline estimator that is robust to outliers caused by eyelashes, eyelids, highlights, and shadows. Secondly, we analyze the relation between iris matching distances and segmentation results by using circular and spline-based methods. Based on our experiments, we found that, even with the proposed robust spline estimator, the improvement of recognition performance is still limited (around 6%). Therefore, in case that less robust spline estimators are used due to the real-time requirement in practical systems, the actual recognition improvement by using splines could be far below the expectation.

A Performance Comparison between Circular and Spline-Based Methods for Iris Segmentation

Synthesis of ergodic, stationary visual patterns is widely applicable in texturing, shape modeling, and digital content creation. The wide applicability of this technique thus requires the pattern synthesis approaches to be scalable, diverse, and authentic. In this paper, we propose an exemplar-based visual pattern synthesis framework that aims to model the inner statistics of visual patterns and generate new, versatile patterns that meet the aforementioned requirements. To this end, we propose an implicit network based on generative adversarial network (GAN) and periodic encoding, thus calling our network the Implicit Periodic Field Network (IPFN). The design of IPFN ensures scalability: the implicit formulation directly maps the input coordinates to features, which enables synthesis of arbitrary size and is computationally efficient for 3D shape synthesis. Learning with a periodic encoding scheme encourages diversity: the network is constrained to model the inner statistics of the exemplar based on spatial latent codes in a periodic field. Coupled with continuously designed GAN training procedures, IPFN is shown to synthesize tileable patterns with smooth transitions and local variations. Last but not least, thanks to both the adversarial training technique and the encoded Fourier features, IPFN learns high-frequency functions that produce authentic, high-quality results. To validate our approach, we present novel experimental results on various applications in 2D texture synthesis and 3D shape synthesis.

Exemplar-based Pattern Synthesis with Implicit Periodic Field Network

OF DISSERTATION 3D Human Face Reconstruction and 2D Appearance Synthesis 3D human face reconstruction has been an extensive research for decades due to its wide applications, such as animation, recognition and 3D-driven appearance synthesis. Although commodity depth sensors are widely available in recent years, image based face reconstruction are significantly valuable as images are much easier to access and store. In this dissertation, we first propose three image-based face reconstruction approaches according to different assumption of inputs. In the first approach, face geometry is extracted from multiple key frames of a video sequence with different head poses. The camera should be calibrated under this assumption. As the first approach is limited to videos, we propose the second approach then focus on single image. This approach also improves the geometry by adding fine grains using shading cue. We proposed a novel albedo estimation and linear optimization algorithm in this approach. In the third approach, we further loose the constraint of the input image to arbitrary in the wild images. Our proposed approach can robustly reconstruct high quality model even with extreme expressions and large poses. We then explore the applicability of our face reconstructions on four interesting applications: video face beautification, Generating personalized facial blendshape from image sequences, face video stylizing and video face replacement. We demonstrate great potentials of our reconstruction approaches on these real-world applications. In particular, with the recent surge of interests in VR/AR, it is increasingly common to see people wearing head-mounted displays. However, the large occlusion on face is a big obstacle for people to communicate in a face-to-face manner. Our another application is that we explore hardware/software solutions for synthesizing the face image with presence of HMDs. We design two setups(experimental and mobile) which integrate two near IR cameras and one color camera to solve this problem. With our algorithm and prototype, we can achieve photo-realistic results. We further propose a deep neutral network to solve the HMD removal problem considering it as a face inpainting problem. This approach doesn’t need special hardware and run in real-time with satisfying results.

3D Human Face Reconstruction and 2D Appearance Synthesis

We present Recurrent Feature Alignment (ReFA), an end-to-end neural network for the very rapid creation of production-grade face assets from multi-view images. ReFA is on par with the industrial pipelines in quality for producing accurate, complete, registered, and textured assets directly applicable to physically-based rendering, but produces the asset end-to-end, fully automatically at a significantly faster speed at 4.5 FPS, which is unprecedented among neural-based techniques. Our method represents face geometry as a position map in the UV space. The network first extracts per-pixel features in both the multi-view image space and the UV space. A recurrent module then iteratively optimizes the geometry by projecting the image-space features to the UV space and comparing them with a reference UV-space feature. The optimized geometry then provides pixel-aligned signals for the inference of high-resolution textures. Experiments have validated that ReFA achieves a median error of 0.603mm in geometry reconstruction, is robust to extreme pose and expression, and excels in sparse-view settings. We believe that the progress achieved by our network enables lightweight, fast face assets acquisition that significantly boosts the downstream applications, such as avatar creation and facial performance capture. It will also enable massive database capturing for deep learning purposes.

Rapid Face Asset Acquisition with Recurrent Feature Alignment

A deep generative model that describes human motions can benefit a wide range of fundamental computer vision and graphics tasks, such as providing robustness to video-based human pose estimation, predicting complete body movements for motion capture systems during occlusions, and assisting key frame animation with plausible movements. In this paper, we present a method for learning complex human motions independent of specific tasks using a combined global and local latent space to facilitate coarse and fine-grained modeling. Specifically, we propose a hierarchical motion variational autoencoder (HM-VAE) that consists of a 2-level hierarchical latent space. While the global latent space captures the overall global body motion, the local latent space enables to capture the refined poses of the different body parts. We demonstrate the effectiveness of our hierarchical motion variational autoencoder in a variety of tasks including video-based human pose estimation, motion completion from partial observations, and motion synthesis from sparse key-frames. Even though, our model has not been trained for any of these tasks specifically, it provides superior performance than task-specific alternatives. Our general-purpose human motion prior model can fix corrupted human body animations and generate complete movements from incomplete observations.

Yajie Zhao

Papers

A Performance Comparison between Circular and Spline-Based Methods for Iris Segmentation

Exemplar-based Pattern Synthesis with Implicit Periodic Field Network

3D Human Face Reconstruction and 2D Appearance Synthesis

Rapid Face Asset Acquisition with Recurrent Feature Alignment

Task-Generic Hierarchical Human Motion Prior using VAEs.