GenAI4ELab/papercli-papers huggingface dataset:
GeoMAE: Masked Geometric Target Prediction for Self-Supervised Point Cloud Pre-Training
Authors: Xiaoyu Tian; Haoxi Ran; Yue Wang; Hang Zhao
Venue: CVPR 2023 (main)

Abstract: This paper tries to address a fundamental question in point cloud self-supervised learning: what is a good signal we should leverage to learn features from point clouds without annotations? To answer that, we introduce a point cloud representation learning framework, based on geometric feature reconstruction. In contrast to recent papers that directly adopt masked autoencoder (MAE) and only predict original coordinates or occupancy from masked point clouds, our method revisits differences between images and point clouds and identifies three self-supervised learning objectives peculiar to point clouds, namely centroid prediction, normal estimation, and curvature prediction. Combined, these three objectives yield an nontrivial self-supervised learning task and mutually facilitate models to better reason fine-grained geometry of point clouds. Our pipeline is conceptually simple and it consists of two major steps: first, it randomly masks out groups of points, followed by a Transformer-based point cloud encoder; second, a lightweight Transformer decoder predicts centroid, normal, and curvature for points in each voxel. We transfer the pre-trained Transformer encoder to a downstream peception model. On the nuScene Datset, our model achieves 3.38 mAP improvment for object detection, 2.1 mIoU gain for segmentation, and 1.7 AMOTA gain for multi-object tracking. We also conduct experiments on the Waymo Open Dataset and achieve significant performance improvements over baselines as well.
Learning Conditional Attributes for Compositional Zero-Shot Learning
Authors: Qingsheng Wang; Lingqiao Liu; Chenchen Jing; Hao Chen; Guoqiang Liang; Peng Wang; Chunhua Shen
Venue: CVPR 2023 (main)

Abstract: Compositional Zero-Shot Learning (CZSL) aims to train models to recognize novel compositional concepts based on learned concepts such as attribute-object combinations. One of the challenges is to model attributes interacted with different objects, e.g., the attribute "wet" in "wet apple" and "wet cat" is different. As a solution, we provide analysis and argue that attributes are conditioned on the recognized object and input image and explore learning conditional attribute embeddings by a proposed attribute learning framework containing an attribute hyper learner and an attribute base learner. By encoding conditional attributes, our model enables to generate flexible attribute embeddings for generalization from seen to unseen compositions. Experiments on CZSL benchmarks, including the more challenging C-GQA dataset, demonstrate better performances compared with other state-of-the-art approaches and validate the importance of learning conditional attributes.
Complete 3D Human Reconstruction From a Single Incomplete Image
Authors: Junying Wang; Jae Shin Yoon; Tuanfeng Y. Wang; Krishna Kumar Singh; Ulrich Neumann
Venue: CVPR 2023 (main)

Abstract: This paper presents a method to reconstruct a complete human geometry and texture from an image of a person with only partial body observed, e.g., a torso. The core challenge arises from the occlusion: there exists no pixel to reconstruct where many existing single-view human reconstruction methods are not designed to handle such invisible parts, leading to missing data in 3D. To address this challenge, we introduce a novel coarse-to-fine human reconstruction framework. For coarse reconstruction, explicit volumetric features are learned to generate a complete human geometry with 3D convolutional neural networks conditioned by a 3D body model and the style features from visible parts. An implicit network combines the learned 3D features with the high-quality surface normals enhanced from multiview to produce fine local details, e.g., high-frequency wrinkles. Finally, we perform progressive texture inpainting to reconstruct a complete appearance of the person in a view-consistent way, which is not possible without the reconstruction of a complete geometry. In experiments, we demonstrate that our method can reconstruct high-quality 3D humans, which is robust to occlusion.
PVT-SSD: Single-Stage 3D Object Detector With Point-Voxel Transformer
Authors: Honghui Yang; Wenxiao Wang; Minghao Chen; Binbin Lin; Tong He; Hua Chen; Xiaofei He; Wanli Ouyang
Venue: CVPR 2023 (main)

Abstract: Recent Transformer-based 3D object detectors learn point cloud features either from point- or voxel-based representations. However, the former requires time-consuming sampling while the latter introduces quantization errors. In this paper, we present a novel Point-Voxel Transformer for single-stage 3D detection (PVT-SSD) that takes advantage of these two representations. Specifically, we first use voxel-based sparse convolutions for efficient feature encoding. Then, we propose a Point-Voxel Transformer (PVT) module that obtains long-range contexts in a cheap manner from voxels while attaining accurate positions from points. The key to associating the two different representations is our introduced input-dependent Query Initialization module, which could efficiently generate reference points and content queries. Then, PVT adaptively fuses long-range contextual and local geometric information around reference points into content queries. Further, to quickly find the neighboring points of reference points, we design the Virtual Range Image module, which generalizes the native range image to multi-sensor and multi-frame. The experiments on several autonomous driving benchmarks verify the effectiveness and efficiency of the proposed method. Code will be available.
Adaptive Human Matting for Dynamic Videos
Authors: Chung-Ching Lin; Jiang Wang; Kun Luo; Kevin Lin; Linjie Li; Lijuan Wang; Zicheng Liu
Venue: CVPR 2023 (main)

Abstract: The most recent efforts in video matting have focused on eliminating trimap dependency since trimap annotations are expensive and trimap-based methods are less adaptable for real-time applications. Despite the latest tripmap-free methods showing promising results, their performance often degrades when dealing with highly diverse and unstructured videos. We address this limitation by introducing Adaptive Matting for Dynamic Videos, termed AdaM, which is a framework designed for simultaneously differentiating foregrounds from backgrounds and capturing alpha matte details of human subjects in the foreground. Two interconnected network designs are employed to achieve this goal: (1) an encoder-decoder network that produces alpha mattes and intermediate masks which are used to guide the transformer in adaptively decoding foregrounds and backgrounds, and (2) a transformer network in which long- and short-term attention combine to retain spatial and temporal contexts, facilitating the decoding of foreground details. We benchmark and study our methods on recently introduced datasets, showing that our model notably improves matting realism and temporal coherence in complex real-world videos and achieves new best-in-class generalizability. Further details and examples are available at https://github.com/microsoft/AdaM.
Learning Common Rationale To Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems
Authors: Yangyang Shu; Anton van den Hengel; Lingqiao Liu
Venue: CVPR 2023 (main)

Abstract: Self-supervised learning (SSL) strategies have demonstrated remarkable performance in various recognition tasks. However, both our preliminary investigation and recent studies suggest that they may be less effective in learning representations for fine-grained visual recognition (FGVR) since many features helpful for optimizing SSL objectives are not suitable for characterizing the subtle differences in FGVR. To overcome this issue, we propose learning an additional screening mechanism to identify discriminative clues commonly seen across instances and classes, dubbed as common rationales in this paper. Intuitively, common rationales tend to correspond to the discriminative patterns from the key parts of foreground objects. We show that a common rationale detector can be learned by simply exploiting the GradCAM induced from the SSL objective without using any pre-trained object parts or saliency detectors, making it seamlessly to be integrated with the existing SSL process. Specifically, we fit the GradCAM with a branch with limited fitting capacity, which allows the branch to capture the common rationales and discard the less common discriminative patterns. At the test stage, the branch generates a set of spatial weights to selectively aggregate features representing an instance. Extensive experimental results on four visual tasks demonstrate that the proposed method can lead to a significant improvement in different evaluation settings.
Reconstructing Animatable Categories From Videos
Authors: Gengshan Yang; Chaoyang Wang; N. Dinesh Reddy; Deva Ramanan
Venue: CVPR 2023 (main)

Abstract: Building animatable 3D models is challenging due to the need for 3D scans, laborious registration, and manual rigging. Recently, differentiable rendering provides a pathway to obtain high-quality 3D models from monocular videos, but these are limited to rigid categories or single instances. We present RAC, a method to build category-level 3D models from monocular videos, disentangling variations over instances and motion over time. Three key ideas are introduced to solve this problem: (1) specializing a category-level skeleton to instances, (2) a method for latent space regularization that encourages shared structure across a category while maintaining instance details, and (3) using 3D background models to disentangle objects from the background. We build 3D models for humans, cats, and dogs given monocular videos. Project page: gengshan-y.github.io/rac-www/
UDE: A Unified Driving Engine for Human Motion Generation
Authors: Zixiang Zhou; Baoyuan Wang
Venue: CVPR 2023 (main)

Abstract: Generating controllable and editable human motion sequences is a key challenge in 3D Avatar generation. It has been labor-intensive to generate and animate human motion for a long time until learning-based approaches have been developed and applied recently. However, these approaches are still task-specific or modality-specific. In this paper, we propose "UDE", the first unified driving engine that enables generating human motion sequences from natural language or audio sequences (see Fig. 1). Specifically, UDE consists of the following key components: 1) a motion quantization module based on VQVAE that represents continuous motion sequence as discrete latent code, 2) a modality-agnostic transformer encoder that learns to map modality-aware driving signals to a joint space, and 3) a unified token transformer (GPT-like) network to predict the quantized latent code index in an auto-regressive manner. 4) a diffusion motion decoder that takes as input the motion tokens and decodes them into motion sequences with high diversity. We evaluate our method on HumanML3D and AIST++ benchmarks, and the experiment results demonstrate our method achieves state-of-the-art performance.
High-Fidelity 3D Human Digitization From Single 2K Resolution Images
Authors: Sang-Hun Han; Min-Gyu Park; Ju Hong Yoon; Ju-Mi Kang; Young-Jae Park; Hae-Gon Jeon
Venue: CVPR 2023 (main)

Abstract: High-quality 3D human body reconstruction requires high-fidelity and large-scale training data and appropriate network design that effectively exploits the high-resolution input images. To tackle these problems, we propose a simple yet effective 3D human digitization method called 2K2K, which constructs a large-scale 2K human dataset and infers 3D human models from 2K resolution images. The proposed method separately recovers the global shape of a human and its details. The low-resolution depth network predicts the global structure from a low-resolution image, and the part-wise image-to-normal network predicts the details of the 3D human body structure. The high-resolution depth network merges the global 3D shape and the detailed structures to infer the high-resolution front and back side depth maps. Finally, an off-the-shelf mesh generator reconstructs the full 3D human model, which are available at https://github.com/SangHunHan92/2K2K. In addition, we also provide 2,050 3D human models, including texture maps, 3D joints, and SMPL parameters for research purposes. In experiments, we demonstrate competitive performance over the recent works on various datasets.
Co-Salient Object Detection With Uncertainty-Aware Group Exchange-Masking
Authors: Yang Wu; Huihui Song; Bo Liu; Kaihua Zhang; Dong Liu
Venue: CVPR 2023 (main)

Abstract: The traditional definition of co-salient object detection (CoSOD) task is to segment the common salient objects in a group of relevant images. Existing CoSOD models by default adopt the group consensus assumption. This brings about model robustness defect under the condition of irrelevant images in the testing image group, which hinders the use of CoSOD models in real-world applications. To address this issue, this paper presents a group exchange-masking (GEM) strategy for robust CoSOD model learning. With two group of image containing different types of salient object as input, the GEM first selects a set of images from each group by the proposed learning based strategy, then these images are exchanged. The proposed feature extraction module considers both the uncertainty caused by the irrelevant images and group consensus in the remaining relevant images. We design a latent variable generator branch which is made of conditional variational autoencoder to generate uncertainly-based global stochastic features. A CoSOD transformer branch is devised to capture the correlation-based local features that contain the group consistency information. At last, the output of two branches are concatenated and fed into a transformer-based decoder, producing robust co-saliency prediction. Extensive evaluations on co-saliency detection with and without irrelevant images demonstrate the superiority of our method over a variety of state-of-the-art methods.
Tangentially Elongated Gaussian Belief Propagation for Event-Based Incremental Optical Flow Estimation
Authors: Jun Nagata; Yusuke Sekikawa
Venue: CVPR 2023 (main)

Abstract: Optical flow estimation is a fundamental functionality in computer vision. An event-based camera, which asynchronously detects sparse intensity changes, is an ideal device for realizing low-latency estimation of the optical flow owing to its low-latency sensing mechanism. An existing method using local plane fitting of events could utilize the sparsity to realize incremental updates for low-latency estimation; however, its output is merely a normal component of the full optical flow. An alternative approach using a frame-based deep neural network could estimate the full flow; however, its intensive non-incremental dense operation prohibits the low-latency estimation. We propose tangentially elongated Gaussian (TEG) belief propagation (BP) that realizes incremental full-flow estimation. We model the probability of full flow as the joint distribution of TEGs from the normal flow measurements, such that the marginal of this distribution with correct prior equals the full flow. We formulate the marginalization using a message-passing based on the BP to realize efficient incremental updates using sparse measurements. In addition to the theoretical justification, we evaluate the effectiveness of the TEGBP in real-world datasets; it outperforms SOTA incremental quasi-full flow method by a large margin. The code will be open-sourced upon acceptance.
Extracting Class Activation Maps From Non-Discriminative Features As Well
Authors: Zhaozheng Chen; Qianru Sun
Venue: CVPR 2023 (main)

Abstract: Extracting class activation maps (CAM) from a classification model often results in poor coverage on foreground objects, i.e., only the discriminative region (e.g., the "head" of "sheep") is recognized and the rest (e.g., the "leg" of "sheep") mistakenly as background. The crux behind is that the weight of the classifier (used to compute CAM) captures only the discriminative features of objects. We tackle this by introducing a new computation method for CAM that explicitly captures non-discriminative features as well, thereby expanding CAM to cover whole objects. Specifically, we omit the last pooling layer of the classification model, and perform clustering on all local features of an object class, where "local" means "at a spatial pixel position". We call the resultant K cluster centers local prototypes - represent local semantics like the "head", "leg", and "body" of "sheep". Given a new image of the class, we compare its unpooled features to every prototype, derive K similarity matrices, and then aggregate them into a heatmap (i.e., our CAM). Our CAM thus captures all local features of the class without discrimination. We evaluate it in the challenging tasks of weakly-supervised semantic segmentation (WSSS), and plug it in multiple state-of-the-art WSSS methods, such as MCTformer and AMN, by simply replacing their original CAM with ours. Our extensive experiments on standard WSSS benchmarks (PASCAL VOC and MS COCO) show the superiority of our method: consistent improvements with little computational overhead.
BlendFields: Few-Shot Example-Driven Facial Modeling
Authors: Kacper Kania; Stephan J. Garbin; Andrea Tagliasacchi; Virginia Estellers; Kwang Moo Yi; Julien Valentin; Tomasz Trzciński; Marek Kowalski
Venue: CVPR 2023 (main)

Abstract: Generating faithful visualizations of human faces requires capturing both coarse and fine-level details of the face geometry and appearance. Existing methods are either data-driven, requiring an extensive corpus of data not publicly accessible to the research community, or fail to capture fine details because they rely on geometric face models that cannot represent fine-grained details in texture with a mesh discretization and linear deformation designed to model only a coarse face geometry. We introduce a method that bridges this gap by drawing inspiration from traditional computer graphics techniques. Unseen expressions are modeled by blending appearance from a sparse set of extreme poses. This blending is performed by measuring local volumetric changes in those expressions and locally reproducing their appearance whenever a similar expression is performed at test time. We show that our method generalizes to unseen expressions, adding fine-grained effects on top of smooth volumetric deformations of a face, and demonstrate how it generalizes beyond faces.
Adaptive Sparse Pairwise Loss for Object Re-Identification
Authors: Xiao Zhou; Yujie Zhong; Zhen Cheng; Fan Liang; Lin Ma
Venue: CVPR 2023 (main)

Abstract: Object re-identification (ReID) aims to find instances with the same identity as the given probe from a large gallery. Pairwise losses play an important role in training a strong ReID network. Existing pairwise losses densely exploit each instance as an anchor and sample its triplets in a mini-batch. This dense sampling mechanism inevitably introduces positive pairs that share few visual similarities, which can be harmful to the training. To address this problem, we propose a novel loss paradigm termed Sparse Pairwise (SP) loss that only leverages few appropriate pairs for each class in a mini-batch, and empirically demonstrate that it is sufficient for the ReID tasks. Based on the proposed loss framework, we propose an adaptive positive mining strategy that can dynamically adapt to diverse intra-class variations. Extensive experiments show that SP loss and its adaptive variant AdaSP loss outperform other pairwise losses, and achieve state-of-the-art performance across several ReID benchmarks. Code is available at https://github.com/Astaxanthin/AdaSP.
NeFII: Inverse Rendering for Reflectance Decomposition With Near-Field Indirect Illumination
Authors: Haoqian Wu; Zhipeng Hu; Lincheng Li; Yongqiang Zhang; Changjie Fan; Xin Yu
Venue: CVPR 2023 (main)

Abstract: Inverse rendering methods aim to estimate geometry, materials and illumination from multi-view RGB images. In order to achieve better decomposition, recent approaches attempt to model indirect illuminations reflected from different materials via Spherical Gaussians (SG), which, however, tends to blur the high-frequency reflection details. In this paper, we propose an end-to-end inverse rendering pipeline that decomposes materials and illumination from multi-view images, while considering near-field indirect illumination. In a nutshell, we introduce the Monte Carlo sampling based path tracing and cache the indirect illumination as neural radiance, enabling a physics-faithful and easy-to-optimize inverse rendering method. To enhance efficiency and practicality, we leverage SG to represent the smooth environment illuminations and apply importance sampling techniques. To supervise indirect illuminations from unobserved directions, we develop a novel radiance consistency constraint between implicit neural radiance and path tracing results of unobserved rays along with the joint optimization of materials and illuminations, thus significantly improving the decomposition performance. Extensive experiments demonstrate that our method outperforms the state-of-the-art on multiple synthetic and real datasets, especially in terms of inter-reflection decomposition.
Towards Professional Level Crowd Annotation of Expert Domain Data
Authors: Pei Wang; Nuno Vasconcelos
Venue: CVPR 2023 (main)

Abstract: Image recognition on expert domains is usually fine-grained and requires expert labeling, which is costly. This limits dataset sizes and the accuracy of learning systems. To address this challenge, we consider annotating expert data with crowdsourcing. This is denoted as PrOfeSsional lEvel cRowd (POSER) annotation. A new approach, based on semi-supervised learning (SSL) and denoted as SSL with human filtering (SSL-HF) is proposed. It is a human-in-the-loop SSL method, where crowd-source workers act as filters of pseudo-labels, replacing the unreliable confidence thresholding used by state-of-the-art SSL methods. To enable annotation by non-experts, classes are specified implicitly, via positive and negative sets of examples and augmented with deliberative explanations, which highlight regions of class ambiguity. In this way, SSL-HF leverages the strong low-shot learning and confidence estimation ability of humans to create an intuitive but effective labeling experience. Experiments show that SSL-HF significantly outperforms various alternative approaches in several benchmarks.