Performance of trimap-free image matting methods is limited when trying to decouple the deterministic and undetermined regions, especially in the scenes where foregrounds are semantically ambiguous, chromaless, or high transmittance. In this paper, we propose a novel framework named Privileged Prior Information Distillation for Image Matting (PPID-IM) that can effectively transfer privileged prior environment-aware information to improve the performance of trimap-free students in solving hard foregrounds. The prior information of trimap regulates only the teacher model during the training stage, while not being fed into the student network during actual inference. To achieve effective privileged cross-modality (i.e. trimap and RGB) information distillation, we introduce a Cross-Level Semantic Distillation (CLSD) module that reinforces the students with more knowledgeable semantic representations and environment-aware information. We also propose an Attention-Guided Local Distillation module that efficiently transfers privileged local attributes from the trimap-based teacher to trimap-free students for the guidance of local-region optimization. Extensive experiments demonstrate the effectiveness and superiority of our PPID on image matting. The code will be released soon.

Creating animatable avatars from static scans requires the modeling of clothing deformations in different poses. Existing learning-based methods typically add pose-dependent deformations upon a minimally-clothed mesh template or a learned implicit template, which have limitations in capturing details or hinder end-to-end learning. In this paper, we revisit point-based solutions and propose to decompose explicit garment-related templates and then add pose-dependent wrinkles to them. In this way, the clothing deformations are disentangled such that the pose-dependent wrinkles can be better learned and applied to unseen poses. Additionally, to tackle the seam artifact issues in recent state-of-the-art point-based methods, we propose to learn point features on a body surface, which establishes a continuous and compact feature space to capture the fine-grained and pose-dependent clothing geometry. To facilitate the research in this field, we also introduce a high-quality scan dataset of humans in real-world clothing. Our approach is validated on two existing datasets and our newly introduced dataset, showing better clothing deformation results in unseen poses. The project page with code and dataset can be found at https://www.liuyebin.com/closet.

Most existing portrait matting models either require expensive auxiliary information or try to decompose the task into sub-tasks that are usually resource-hungry. These challenges limit its application on low-power computing devices. In this paper, we propose an ultra-light-weighted portrait matting network via parallel semantic guidance (PSGNet) for real-time portrait matting without any auxiliary inputs. PSGNet leverages parallel multi-level semantic information to efficiently guide the feature representations to replace traditional sequential semantic hints from objective decomposition. We also introduce an efficient fusion module to effectively combine parallel branches of PSGNet to minimize the representation redundancy. Comprehensive experiments demonstrate that our PSGNet can achieve remarkable performance on both synthetic and real-world images. Our PSGNet is capable to process at 100fps thanks to its ultra-small number of parameters, which makes it deployable on low-power computing devices without compromising on the performance of real-time portrait matting.

Creating relightable and animatable human characters from monocular video at a low cost is a critical task for digital human modeling and virtual reality applications. This task is complex due to intricate articulation motion, a wide range of ambient lighting conditions, and pose-dependent clothing deformations. In this paper, we introduce a novel self-supervised framework that takes a monocular video of a moving human as input and generates a 3D neural representation capable of being rendered with novel poses under arbitrary lighting conditions. Our framework decomposes dynamic humans under varying illumination into neural fields in canonical space, taking into account geometry and spatially varying BRDF material properties. Additionally, we introduce pose-driven deformation fields, enabling bidirectional mapping between canonical space and observation. Leveraging the proposed appearance decomposition and deformation fields, our framework learns in a self-supervised manner. Ultimately, based on pose-driven deformation, recovered appearance, and physically-based rendering, the reconstructed human figure becomes relightable and can be explicitly driven by novel poses. We demonstrate significant performance improvements over previous works and provide compelling examples of relighting from monocular videos of moving humans in challenging, uncontrolled capture scenarios.

Different from most existing approaches that require trimap generation for each frame, we reformulate video object matting (VOM) by introducing improved semantic guidance propagation. The proposed approach can achieve a higher degree of temporal coherence between frames with only a single coarse mask as a reference. In this paper, we adapt the hierarchical memory matching mechanism into the space-time baseline to build an efficient and robust framework for semantic guidance propagation and alpha prediction. To enhance the temporal smoothness, we also propose a cross-frame attention refinement (CFAR) module that can refine the feature representations across multiple adjacent frames (both historical and current frames) based on the spatio-temporal correlation among the cross- frame pixels. Extensive experiments demonstrate the effectiveness of hierarchical spatio-temporal semantic guidance and the cross-video-frame attention refinement module, and our model outperforms the state-of-the-art VOM methods. We also analyze the significance of different components in our model.

It is extremely challenging to create an animatable clothed human avatar from RGB videos, especially for loose clothes due to the difficulties in motion modeling. To address this problem, we introduce a novel representation on the basis of recent neural scene rendering techniques. The core of our representation is a set of structured local radiance fields, which are anchored to the pre-defined nodes sampled on a statistical human body template. These local radiance fields not only leverage the flexibility of implicit representation in shape and appearance modeling, but also factorize cloth deformations into skeleton motions, node residual translations and the dynamic detail variations inside each individual radiance field. To learn our representation from RGB data and facilitate pose generalization, we propose to learn the node translations and the detail variations in a conditional generative latent space. Overall, our method enables automatic construction of animatable human avatars for various types of clothes without the need for scanning subject-specific templates, and can generate realistic images with dynamic details for novel poses. Experiment show that our method outperforms state-of-the-art methods both qualitatively and quantitatively.

Most existing CNN-based salient object detection methods can identify fine-grained segmentation details like hair and animal fur, but often mispredict the salient object due to lack of global contextual information caused by locality convolution layers. The limited training data of the current SOD task adds additional difficulty to capture the saliency information. In this paper, we propose a two-stage predict-refine SDETR model to leverage both benefits of transformer and CNN layers that can produce results with accurate saliency prediction and fine-grained local details. We also propose a novel pre-train dataset annotation COCO SOD to erase the overfitting problem caused by insufficient training data. Comprehensive experiments on five benchmark datasets demonstrate that the SDETR outperforms state-of-the-art approaches on four evaluation metrics, and our COCO SOD can largely improve the model performance on DUTS, ECSSD, DUT, PASCAL-S datasets.

We propose CrossHuman, a novel method that learns cross-guidance from parametric human model and multi-frame RGB images to achieve high-quality 3D human reconstruction. To recover geometry details and texture even in invisible regions, we design a reconstruction pipeline combined with tracking-based methods and tracking-free methods. Given a monocular RGB sequence, we track the parametric human model in the whole sequence, the points (voxels) corresponding to the target frame are warped to reference frames by the parametric body motion. Guided by the geometry priors of the parametric body and spatially aligned features from RGB sequence, the robust implicit surface is fused. Moreover, a multi-frame transformer (MFT) and a self-supervised warp refinement module are integrated to the framework to relax the requirements of parametric body and help to deal with very loose cloth. Compared with previous works, our CrossHuman enables high-fidelity geometry details and texture in both visible and invisible regions and improves the accuracy of the human reconstruction even under estimated inaccurate parametric human models. The experiments demonstrate that our method achieves state-of-the-art (SOTA) performance.

Most automatic matting methods try to separate the salient foreground from the background. However, the insufficient quantity and subjective bias of the current existing matting datasets make it difficult to fully explore the semantic association between object-to-object and object-to-environment in a given image. In this paper, we propose a Situational Perception Guided Image Matting (SPG-IM) method that mitigates subjective bias of matting annotations and captures sufficient situational perception information for better global saliency distilled from the visual-to-textual task. SPG-IM can better associate inter-objects and object-to-environment saliency, and compensate the subjective nature of image matting and its expensive annotation. We also introduce a textual Semantic Transformation (TST) module that can effectively transform and integrate the semantic feature stream to guide the visual representations. In addition, an Adaptive Focal Transformation (AFT) Refinement Network is proposed to adaptively switch multi-scale receptive fields and focal points to enhance both global and local details. Extensive experiments demonstrate the effectiveness of situational perception guidance from the visual-to-textual tasks on image matting, and our model outperforms the state-of-the-art methods. We also analyze the significance of different components in our model.

We propose FloRen, a novel system for real-time, high-resolution free-view human synthesis. Our system runs at 15fps in 1K resolution with very sparse RGB cameras. In FloRen, a coarse-level implicit geometry is recovered at first as initialization, and then processed by a neural rendering framework based on appearance flow. Our appearance flow-based rendering framework consists of three steps, namely view-dependent depth refinement, appearance flow estimation and occlusion-aware color rendering. In this way, we resolve the view synthesis problem in the image plane, where 2D convolutional neural networks can be efficiently applied, contributing to high speed performance. For robust appearance flow estimation, we explicitly combine data-driven human prior knowledge with multiview geometric constraints. The accurate appearance flow enables precise color mapping from input view to novel view, which greatly facilitates high-resolution novel view generation. We demonstrate that our system achieves state-of-the-art performance and even outperforms many offline methods.

Several video-based 3D pose and shape estimation algorithms have been proposed to resolve the temporal inconsistency of single-image-based methods. However it still remains challenging to have stable and accurate reconstruction. In this paper, we propose a new framework Deep Two-Stream Video Inference for Human Body Pose and Shape Estimation (DTS-VIBE), to generate 3D human pose and mesh from RGB videos. We reformulate the task as a multi-modality problem that fuses RGB and optical flow for more reliable estimation. In order to fully utilize both sensory modalities (RGB or optical flow), we train a two-stream temporal network based on transformer to predict SMPL parameters. The supplementary modality, optical flow, helps to maintain temporal consistency by leveraging motion knowledge between two consecutive frames. The proposed algorithm is extensively evaluated on the Human3.6 and 3DPW datasets. The experimental results show that it outperforms other state-of-the-art methods by a significant margin.

Most existing human matting algorithms tried to separate pure human-only foreground from the background. In this paper, we propose a Virtual Multi-modality Foreground Matting (VMFM) method to learn human-object interactive foreground (human and objects interacted with him or her) from a raw RGB image. The VMFM method requires no additional inputs, e.g. trimap or known background. We reformulate foreground matting as a self-supervised multi-modality problem: factor each input image into estimated depth map, segmentation mask, and interaction heatmap using three auto-encoders. In order to fully utilize the characteristics of each modality, we first train a dual encoder-to-decoder network to estimate the same alpha matte. Then we introduce a self-supervised method: Complementary Learning(CL) to predict deviation probability map and exchange reliable gradients across modalities without label. We conducted extensive experiments to analyze the effectiveness of each modality and the significance of different components in complementary learning. We demonstrate that our model outperforms the state-of-the-art methods.

Unconstrained remote gaze estimation remains challenging mostly due to its vulnerability to the large variability in head-pose. Prior solutions struggle to maintain reliable accuracy in unconstrained remote gaze tracking. Among them, appearance-based solutions demonstrate tremendous potential in improving gaze accuracy. However, existing works still suffer from head movement and are not robust enough to handle real-world scenarios. Especially most of them study gaze estimation under controlled scenarios where the collected datasets often cover limited ranges of both head-pose and gaze which introduces further bias. In this paper, we propose novel end-to-end appearance-based gaze estimation methods that could more robustly incorporate different levels of head-pose representations into gaze estimation. Our method could generalize to real-world scenarios with low image quality, different lightings and scenarios where direct head-pose information is not available. To better demonstrate the advantage of our methods, we further propose a new benchmark dataset with the most rich distribution of head-gaze combination reflecting real-world scenarios. Extensive evaluations on several public datasets and our own dataset demonstrate that our method consistently outperforms the state-of-the-art by a significant margin.