GenAI4ELab/papercli-papers huggingface dataset:
UIA-ViT: Unsupervised Inconsistency-Aware Method Based on Vision Transformer for Face Forgery Detection
Authors: Wanyi Zhuang; Qi Chu; Zhentao Tan; Qiankun Liu; Haojie Yuan; Changtao Miao; Zixiang Luo; Nenghai Yu
Venue: ECCV 2022 (main)

Abstract: "Intra-frame inconsistency has been proved to be effective for the generalization of face forgery detection. However, learning to focus on these inconsistency requires extra pixel-level forged location annotations. Acquiring such annotations is non-trivial. Some existing methods generate large-scale synthesized data with location annotations, which is time-consuming. Others generate forgery location labels by subtracting paired real and fake images, yet such paired data is difficult to collected and the generated label is usually discontinuous. To overcome these limitations, we propose a novel Unsupervised Inconsistency-Aware method based on Vision Transformer, called UIA-ViT. Thanks to the self-attention mechanism, the attention map among patch embeddings naturally represents the consistency relation, making the vision Transformer suitable for the consistency representation learning. Specifically, we propose two key components: Unsupervised Patch Consistency Learning (UPCL) and Progressive Consistency Weighted Assemble (PCWA). UPCL is designed for learning the consistency-related representation with progressive optimized pseudo annotations that are derived from multivariate Gaussian estimation. PCWA enhances the final classification embedding with previous patch embeddings optimized by UPCL to further improve the detection performance. Extensive experiments demonstrate the effectiveness of the proposed method."
Effective Presentation Attack Detection Driven by Face Related Task
Authors: Wentian Zhang; Haozhe Liu; Feng Liu; Raghavendra Ramachandra; Christoph Busch
Venue: ECCV 2022 (main)

Abstract: "The robustness and generalization ability of Presentation Attack Detection (PAD) methods is critical to ensure the security of Face Recognition Systems (FRSs). However, in a real scenario, Presentation Attacks (PAs) are various and it is hard to predict the Presentation Attack Instrument (PAI) species that will be used by the attacker. Existing PAD methods are highly dependent on the limited training set and cannot generalize well to unknown PAI species. Unlike this specific PAD task, other face related tasks trained by huge amount of real faces (e.g. face recognition and attribute editing) can be effectively adopted into different application scenarios. Inspired by this, we propose to trade position of PAD and face related work in a face system and apply the free acquired prior knowledge from face related tasks to solve face PAD, so as to improve the generalization ability in detecting PAs. The proposed method, first introduces task specific features from other face related task, then, we design a Cross-Modal Adapter using a Graph Attention Network (GAT) to re-map such features to adapt to PAD task. Finally, face PAD is achieved by using the hierarchical features from a CNN-based PA detector and the re-mapped features. The experimental results show that the proposed method can achieve significant improvements in the complicated and hybrid datasets, when compared with the state-of-the-art methods. In particular, when training on the datasets OULU-NPU, CASIA-FASD, and Idiap Replay-Attack, we obtain HTER (Half Total Error Rate) of 5.48% for the testing dataset MSU-MFSD, outperforming the baseline by 7.39%. The source code is available at https://github.com/WentianZhang-ML/FRT-PAD."
PPT: Token-Pruned Pose Transformer for Monocular and Multi-View Human Pose Estimation
Authors: Haoyu Ma; Zhe Wang; Yifei Chen; Deying Kong; Liangjian Chen; Xingwei Liu; Xiangyi Yan; Hao Tang; Xiaohui Xie
Venue: ECCV 2022 (main)

Abstract: "Recently, the vision transformer and its variants have played an increasingly important role in both monocular and multi-view human pose estimation. Considering image patches as tokens, transformers can model the global dependencies within the entire image or across images from other views. However, global attention is computationally expensive. As a consequence, it is difficult to scale up these transformer-based methods to high-resolution features and many views. In this paper, we propose the token-Pruned Pose Transformer (PPT) for 2D human pose estimation, which can locate a rough human mask and performs self-attention only within selected tokens. Furthermore, we extend our PPT to multi-view human pose estimation. Built upon PPT, we propose a new cross-view fusion strategy, called human area fusion, which considers all human foreground pixels as corresponding candidates. Experimental results on COCO and MPII demonstrate that our PPT can match the accuracy of previous pose transformer methods while reducing the computation. Moreover, experiments on Human 3.6M and Ski-Pose demonstrate that our Multi-view PPT can efficiently fuse cues from multiple views and achieve new state-of-the-art results. Source code and trained model can be found at \url{https://github.com/HowieMa/PPT}."
AvatarPoser: Articulated Full-Body Pose Tracking from Sparse Motion Sensing
Authors: Jiaxi Jiang; Paul Streli; Huajian Qiu; Andreas Fender; Larissa Laich; Patrick Snape; Christian Holz
Venue: ECCV 2022 (main)

Abstract: "Today’s Mixed Reality head-mounted displays track the user’s head pose in world space as well as the user’s hands for interaction in both Augmented Reality and Virtual Reality scenarios. While this is adequate to support user input, it unfortunately limits users’ virtual representations to just their upper bodies. Current systems thus resort to floating avatars, whose limitation is particularly evident in collaborative settings. To estimate full-body poses from the sparse input sources, prior work has incorporated additional trackers and sensors at the pelvis or lower body, which increases setup complexity and limits practical application in mobile settings. In this paper, we present AvatarPoser, the first learning-based method that predicts full-body poses in world coordinates using only motion input from the user’s head and hands. Our method builds on a Transformer encoder to extract deep features from the input signals and decouples global motion from the learned local joint orientations to guide pose estimation. To obtain accurate full-body motions that resemble motion capture animations, we refine the arm joints’ positions using an optimization routine with inverse kinematics to match the original tracking input. In our evaluation, AvatarPoser achieved new state-of-the-art results in evaluations on large motion capture datasets (AMASS). At the same time, our method’s inference speed supports real-time operation, providing a practical interface to support holistic avatar control and representation for Metaverse applications."
P-STMO: Pre-trained Spatial Temporal Many-to-One Model for 3D Human Pose Estimation
Authors: Wenkang Shan; Zhenhua Liu; Xinfeng Zhang; Shanshe Wang; Siwei Ma; Wen Gao
Venue: ECCV 2022 (main)

Abstract: "This paper introduces a novel Pre-trained Spatial Temporal Many-to-One (P-STMO) model for 2D-to-3D human pose estimation task. To reduce the difficulty of capturing spatial and temporal information, we divide this task into two stages: pre-training (Stage I) and fine-tuning (Stage II). In Stage I, a self-supervised pre-training sub-task, termed masked pose modeling, is proposed. The human joints in the input sequence are randomly masked in both spatial and temporal domains. A general form of denoising auto-encoder is exploited to recover the original 2D poses and the encoder is capable of capturing spatial and temporal dependencies in this way. In Stage II, the pre-trained encoder is loaded to STMO model and fine-tuned. The encoder is followed by a many-to-one frame aggregator to predict the 3D pose in the current frame. Especially, an MLP block is utilized as the spatial feature extractor in STMO, which yields better performance than other methods. In addition, a temporal downsampling strategy is proposed to diminish data redundancy. Extensive experiments on two benchmarks show that our method outperforms state-of-the-art methods with fewer parameters and less computational overhead. For example, our P-STMO model achieves 42.1mm MPJPE on Human3.6M dataset when using 2D poses from CPN as inputs. Meanwhile, it brings a 1.5-7.1× speedup to state-of-the-art methods. Code is available at https://github.com/paTRICK-swk/P-STMO."
D&D: Learning Human Dynamics from Dynamic Camera
Authors: Jiefeng Li; Siyuan Bian; Chao Xu; Gang Liu; Gang Yu; Cewu Lu
Venue: ECCV 2022 (main)

Abstract: "3D human pose estimation from a monocular video has recently seen significant improvements. However, most state-of-the-art methods are kinematics-based, which are prone to physically implausible motions with pronounced artifacts. Current dynamics-based methods can predict physically plausible motion but are restricted to simple scenarios with static camera view. In this work, we present D&D (Learning Human Dynamics from Dynamic Camera), which leverages the laws of physics to reconstruct 3D human motion from the in-the-wild videos with a moving camera. D&D introduces inertial force control (IFC) to explain the 3D human motion in the non-inertial local frame by considering the inertial forces of the dynamic camera. To learn the ground contact with limited annotations, we develop probabilistic contact torque (PCT), which is computed by differentiable sampling from contact probabilities and used to generate motions. The contact state can be weakly supervised by encouraging the model to generate correct motions. Furthermore, we propose an attentive PD controller that adjusts target pose states using temporal information to obtain smooth and accurate pose control. Our approach is entirely neural-based and runs without offline optimization or simulation in physics engines. Experiments on large-scale 3D human motion benchmarks demonstrate the effectiveness of D&D, where we exhibit superior performance against both state-of-the-art kinematics-based and dynamics-based methods. Code is available at https://github.com/Jeff-sjtu/DnD"
Explicit Occlusion Reasoning for Multi-Person 3D Human Pose Estimation
Authors: Qihao Liu; Yi Zhang; Song Bai; Alan Yuille
Venue: ECCV 2022 (main)

Abstract: "Occlusion poses a great threat to monocular multi-person 3D human pose estimation due to large variability in terms of the shape, appearance, and position of occluders. While existing methods try to handle occlusion with pose priors/constraints, data augmentation, or implicit reasoning, they still fail to generalize to unseen poses or occlusion cases and may make large mistakes when multiple people are present. Inspired by the remarkable ability of humans to infer occluded joints from visible cues, we develop a method to explicitly model this process that significantly improves bottom-up multi-person human pose estimation with or without occlusions. First, we split the task into two subtasks: visible keypoints detection and occluded keypoints reasoning, and propose a Deeply Supervised Encoder Distillation (DSED) network to solve the second one. To train our model, we propose a Skeleton-guided human Shape Fitting (SSF) approach to generate pseudo occlusion labels on the existing datasets, enabling explicit occlusion reasoning. Experiments show that explicitly learning from occlusions improves human pose estimation. In addition, exploiting feature-level information of visible joints allows us to reason about occluded joints more accurately. Our method outperforms both the state-of-the-art top-down and bottom-up methods on several benchmarks."
COUCH: Towards Controllable Human-Chair Interactions
Authors: Xiaohan Zhang; Bharat Lal Bhatnagar; Sebastian Starke; Vladimir Guzov; Gerard Pons-Moll
Venue: ECCV 2022 (main)

Abstract: "Humans can interact with an object in the scene in many different ways, which are often associated with different modalities of contacting with the object. This creates a highly complex motion space that can be difficult to learn, particularly when synthesizing such human interactions in a controllable manner. Existing works on synthesizing human scene interaction focus on the high-level control of interacting with a particular object without considering fine-grained control of limb motion variations within one task. In this work, we drive this direction and study the problem of synthesizing scene interactions conditioned on a wide range of contact positions on the object. We pick human-chair interactions as an example. We propose a novel synthesis framework COUCH, which firstly plans ahead the motion by predicting contact-aware control signals of the hands, which are then used to synthesize contact-conditioned interactions. Furthermore, we contribute a large human-chair interaction dataset with clean annotations, the COUCH Dataset. Our method shows consistent quantitative and qualitative improvements over existing methods for learning human-object interactions."
Identity-Aware Hand Mesh Estimation and Personalization from RGB Images
Authors: Deying Kong; Linguang Zhang; Liangjian Chen; Haoyu Ma; Xiangyi Yan; Shanlin Sun; Xingwei Liu; Kun Han; Xiaohui Xie
Venue: ECCV 2022 (main)

Abstract: "Reconstructing 3D hand meshes from monocular RGB images has attracted increasing amount of attention due to its enormous potential applications in the field of AR/VR. Most state-of-the-art methods attempt to tackle this task in an anonymous manner. Specifically, the identity of the subject is ignored even though it is practically available in real applications where the user is unchanged in a continuous recording session. In this paper, we propose an identity-aware hand mesh estimation model, which can incorporate the identity information represented by the intrinsic shape parameters of the subject. We demonstrate the importance of the identity information by comparing the proposed identity-aware model to a baseline which treats subject anonymously. Furthermore, to handle the use case where the test subject is unseen, we propose a novel personalization pipeline to calibrate the intrinsic shape parameters using only a few unlabeled RGB images of the subject. Experiments on two large scale public datasets validate the state-of-the-art performance of our proposed method."
C3P: Cross-Domain Pose Prior Propagation for Weakly Supervised 3D Human Pose Estimation
Authors: Cunlin Wu; Yang Xiao; Boshen Zhang; Mingyang Zhang; Zhiguo Cao; Joey Tianyi Zhou
Venue: ECCV 2022 (main)

Abstract: This paper first proposes and solves weakly supervised 3D human pose estimation (HPE) problem in point cloud, via propagating the pose prior within unlabelled RGB-point cloud sequence to 3D domain. Our approach termed C3P does not require any labor-consuming 3D keypoint annotation for training. To this end, we propose to transfer 2D HPE annotation information within the existing large-scale RGB datasets (e.g., MS COCO) to 3D task, using unlabelled RGB-point cloud sequence easy to acquire for linking 2D and 3D domains. The self-supervised 3D HPE clues within point cloud sequence are also exploited, concerning spatial-temporal constraints on human body symmetry, skeleton length and joints’ motion. And, a refined point set network structure for weakly supervised 3D HPE is proposed in encoder-decoder manner. The experiments on CMU Panoptic and ITOP datasets demonstrate that, our method can achieve the comparable results to the 3D fully supervised state-of-the-art counterparts. When large-scale unlabelled data (e.g., NTU RGB+D 60) is used, our approach can even outperform them under the more challenging cross-setup test setting. The source code is released at "https://github.com/wucunlin/C3P" for research use only.
Pose-NDF: Modeling Human Pose Manifolds with Neural Distance Fields
Authors: Garvita Tiwari; Dimitrije Antić; Jan Eric Lenssen; Nikolaos Sarafianos; Tony Tung; Gerard Pons-Moll
Venue: ECCV 2022 (main)

Abstract: "We present Pose-NDF, a continuous model for plausible human poses based on neural distance fields (NDFs). Pose or motion priors are important for generating realistic new poses and for reconstructing accurate poses from noisy or partial observations. Pose-NDF learns a manifold of plausible poses as the zero level set of a neural implicit function, extending the idea of modelling implicit surfaces in 3D to the high dimensional domain SO(3)K, where a human pose is defined by a single data point, represented by K quaternions. The resulting high dimensional implicit function can be differentiated with respect to the input poses and thus can be used to project arbitrary poses onto the manifold by using gradient descent on the set of 3-dimensional hyperspheres. In contrast to previous VAE-based human pose priors, which transform the pose space into a Gaussian distribution, we model the actual pose manifold, preserving the distances between poses. We demonstrate that this approach and thus, Pose-NDF, outperforms existing state-of-the-art methods as a prior in various downstream tasks, ranging from denoising real world human mocap data, pose recovery from occluded data to 3D pose reconstruction from images. Furthermore, we show that it can be used to generate more diverse poses by random sampling and projection than VAE based methods. We will release our code and pre-trained model for further research."
CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation
Authors: Zhihao Li; Jianzhuang Liu; Zhensong Zhang; Songcen Xu; Youliang Yan
Venue: ECCV 2022 (main)

Abstract: "Top-down methods dominate the field of 3D human pose and shape estimation, because they are decoupled from human detection and allow researchers to focus on the core problem. However, cropping, their first step, discards the location information from the very beginning, which makes themselves unable to accurately predict the global rotation in the original camera coordinate system. To address this problem, we propose to Carry Location Information in Full Frames (CLIFF) into this task. Specifically, we feed more holistic features to CLIFF by concatenating the cropped image feature with its bounding box information. We calculate the 2D reprojection loss with a broader view of the full frame, taking a projection process similar to that of the person projected in the image. Fed and supervised by global-location-aware information, CLIFF directly predicts the global rotation along with more accurate articulated poses. Besides, we propose a pseudo-ground-truth annotator based on CLIFF, which provides high-quality 3D annotations for in-the-wild 2D datasets and offers crucial full supervision for regression-based methods. Extensive experiments on popular benchmarks show that CLIFF outperforms prior arts by a significant margin, and reaches the first place on the AGORA leaderboard (the SMPL-Algorithms track)."
DeciWatch: A Simple Baseline for 10× Efficient 2D and 3D Pose Estimation
Authors: Ailing Zeng; Xuan Ju; Lei Yang; Ruiyuan Gao; Xizhou Zhu; Bo Dai; Qiang Xu
Venue: ECCV 2022 (main)

Abstract: "This paper proposes a simple baseline framework for video-based 2D/3D human pose estimation that can achieve 10 times efficiency improvement over existing works without any performance degradation, named DeciWatch. Unlike current solutions that estimate each frame in a video, DeciWatch introduces a simple yet effective sample-denoise-recover framework that only watches sparsely sampled frames, taking advantage of the continuity of human motions and the lightweight pose representation. Specifically, DeciWatch uniformly samples less than 10% video frames for detailed estimation, denoises the estimated 2D/3D poses with an efficient Transformer architecture, and then accurately recovers the rest of the frames using another Transformer-based network. Comprehensive experimental results on three video-based human pose estimation and body mesh recovery tasks with four datasets validate the efficiency and effectiveness of DeciWatch. Code is available at https://github.com/cure-lab/DeciWatch."
SmoothNet: A Plug-and-Play Network for Refining Human Poses in Videos
Authors: Ailing Zeng; Lei Yang; Xuan Ju; Jiefeng Li; Jianyi Wang; Qiang Xu
Venue: ECCV 2022 (main)

Abstract: "When analyzing human motion videos, the output jitters from existing pose estimators are highly-unbalanced with varied estimation errors across frames. Most frames in a video are relatively easy to estimate and only suffer from slight jitters. In contrast, for rarely seen or occluded actions, the estimated positions of multiple joints largely deviate from the ground truth values for a consecutive sequence of frames, rendering significant jitters on them. To tackle this problem, we propose to attach a dedicated temporal-only refinement network to existing pose estimators for jitter mitigation, named SmoothNet. Unlike existing learning-based solutions that employ spatio-temporal models to co-optimize per-frame precision and temporal smoothness at all the joints, SmoothNet models the natural smoothness characteristics in body movements by learning the long-range temporal relations of every joint without considering the noisy correlations among joints. With a simple yet effective motion-aware fully-connected network, SmoothNet improves the temporal smoothness of existing pose estimators significantly and enhances the estimation accuracy of those challenging frames as a side-effect. Moreover, as a temporal-only model, a unique advantage of SmoothNet is its strong transferability across various types of estimators and datasets. Comprehensive experiments on five datasets with eleven popular backbone networks across 2D and 3D pose estimation and body recovery tasks demonstrate the efficacy of the proposed solution. Code is available at https://github.com/cure-lab/SmoothNet."
PoseTrans: A Simple yet Effective Pose Transformation Augmentation for Human Pose Estimation
Authors: Wentao Jiang; Sheng Jin; Wentao Liu; Chen Qian; Ping Luo; Si Liu
Venue: ECCV 2022 (main)

Abstract: "Human pose estimation aims to accurately estimate a wide variety of human poses. However, existing datasets often follow a long-tailed distribution that unusual poses only occupy a small portion, which further leads to the lack of diversity of rare poses. These issues result in the inferior generalization ability of current pose estimators. In this paper, we present a simple yet effective data augmentation method, termed Pose Transformation (PoseTrans), to alleviate the aforementioned problems. Specifically, we propose Pose Transformation Module (PTM) to create new training samples that have diverse poses and adopt a pose discriminator to ensure the plausibility of the augmented poses. Besides, we propose Pose Clustering Module (PCM) to measure the pose rarity and select the “rarest” poses to help balance the long-tailed distribution. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our method, especially on rare poses. Also, our method is efficient and simple to implement, which can be easily integrated into the training pipeline of existing pose estimation models."