GenAI4ELab/papercli-papers huggingface dataset:
AutoAvatar: Autoregressive Neural Fields for Dynamic Avatar Modeling
Authors: Ziqian Bai; Timur Bagautdinov; Javier Romero; Michael Zollhöfer; Ping Tan; Shunsuke Saito
Venue: ECCV 2022 (main)

Abstract: "Neural fields such as implicit surfaces have recently enabled avatar modeling from raw scans without explicit temporal correspondences. In this work, we exploit autoregressive modeling to further extend this notion to capture dynamic effects, such as soft-tissue deformations. Although autoregressive models are naturally capable of handling dynamics, it is non-trivial to apply them to implicit representations, as explicit state decoding is infeasible due to prohibitive memory requirements. In this work, for the first time, we enable autoregressive modeling of implicit avatars. To reduce the memory bottleneck and efficiently model dynamic implicit surfaces, we introduce the notion of articulated observer points, which relate implicit states to the explicit surface of a parametric human body model. We demonstrate that encoding implicit surfaces as a set of height fields defined on articulated observer points leads to significantly better generalization compared to a latent representation. The experiments show that our approach outperforms the state of the art, achieving plausible dynamic deformations even for unseen motions. https://zqbai-jeremy.github.io/autoavatar"
Deep Radial Embedding for Visual Sequence Learning
Authors: Yuecong Min; Peiqi Jiao; Yanan Li; Xiaotao Wang; Lei Lei; Xiujuan Chai; Xilin Chen
Venue: ECCV 2022 (main)

Abstract: "Connectionist Temporal Classification (CTC) is a popular objective function in sequence recognition, which provides supervision for unsegmented sequence data through aligning sequence and its corresponding labeling iteratively. The blank class of CTC plays a crucial role in the alignment process and is often considered responsible for the peaky behavior of CTC. In this study, we propose an objective function named RadialCTC that constrains sequence features on a hypersphere while retaining the iterative alignment mechanism of CTC. The learned features of each non-blank class are distributed on a radial arc from the center of the blank class, which provides a clear geometric interpretation and makes the alignment process more efficient. Besides, RadialCTC can control the peaky behavior by simply modifying the logit of the blank class. Experimental results of recognition and localization demonstrate the effectiveness of RadialCTC on two sequence recognition applications."
SAGA: Stochastic Whole-Body Grasping with Contact
Authors: Yan Wu; Jiahao Wang; Yan Zhang; Siwei Zhang; Otmar Hilliges; Fisher Yu; Siyu Tang
Venue: ECCV 2022 (main)

Abstract: "The synthesis of human grasping has numerous applications including AR/VR, video games and robotics. While methods have been proposed to generate realistic hand-object interaction for object grasping and manipulation, these typically only consider interacting hand alone. Our goal is to synthesize whole-body grasping motions. Starting from an arbitrary initial pose, we aim to generate diverse and natural whole-body human motions to approach and grasp a target object in 3D space. This task is challenging as it requires modeling both whole-body dynamics and dexterous finger movements. To this end, we propose SAGA (StochAstic whole-body Grasping with contAct), a framework which consists of two key components: (a) Static whole-body grasping pose generation. Specifically, we propose a multi-task generative model, to jointly learn static whole-body grasping poses and human-object contacts. (b) Grasping motion infilling. Given an initial pose and the generated whole-body grasping pose as the start and end of the motion respectively, we design a novel contact-aware generative motion infilling module to generate a diverse set of grasp-oriented motions. We demonstrate the effectiveness of our method, which is a novel generative framework to synthesize realistic and expressive whole-body motions that approach and grasp randomly placed unseen objects. Code and models are available at https://jiahaoplus.github.io/SAGA/saga.html."
Neural Capture of Animatable 3D Human from Monocular Video
Authors: Gusi Te; Xiu Li; Xiao Li; Jinglu Wang; Wei Hu; Yan Lu
Venue: ECCV 2022 (main)

Abstract: "We present a novel paradigm of building an animatable 3D human representation from a monocular video input, such that it can be rendered in any unseen poses and views. Our method is based on a dynamic Neural Radiance Field (NeRF) rigged by a mesh-based parametric 3D human model serving as a geometry proxy. Previous methods usually rely on multi-view videos or accurate 3D geometry information as additional inputs; besides, most methods suffer from degraded quality when generalized to unseen poses. We identify that the key to generalization is a good input embedding for querying dynamic NeRF: A good input embedding should define an injective mapping in the full volumetric space, guided by surface mesh deformation under pose variation. Based on this observation, we propose to embed the input query with its relationship to local surface regions spanned by a set of geodesic nearest neighbors on mesh vertices. By including both position and relative distance information, our embedding defines a distance-preserved deformation mapping and generalizes well to unseen poses. To reduce the dependency on additional inputs, we first initialize per-frame 3D meshes using off-the-shelf tools and then propose a pipeline to jointly optimize NeRF and refine the initial mesh. Extensive experiments show our method can synthesize plausible human rendering results under unseen poses and views."
General Object Pose Transformation Network from Unpaired Data
Authors: Yukun Su; Guosheng Lin; Ruizhou Sun; Qingyao Wu
Venue: ECCV 2022 (main)

Abstract: "Object pose transformation is a challenging task. Yet, most existing pose transformation networks only focus on synthesizing humans. These methods either rely on the keypoints information or rely on the manual annotations of the paired target pose images for training. However, collecting such paired data is laboring and the cue of keypoints is inapplicable to general objects. In this paper, we address a problem of novel general object pose transformation from unpaired data. Given a source image of an object that provides appearance information and the desired pose image as a reference in the absence of paired examples, we produce a depiction of that object in that pose, retaining the appearance of both the object and background. Specifically, to preserve the source information, we propose an adversarial network with $\textbf{S}$patial-$\textbf{S}$tructural (SS) block and $\textbf{T}$exture-$\textbf{S}$tyle-$\textbf{C}$olor (TSC) block after the correlation matching module that facilitates the output to be semantically corresponding to the target pose image while contextually related to the source image. In addition, we can extend our network to complete multi-object and cross-category pose transformation. Extensive experiments demonstrate the effectiveness of our method which can create more realistic images when compared to those of recent approaches in terms of image quality. Moreover, we show the practicality of our method for several applications."
Compositional Human-Scene Interaction Synthesis with Semantic Control
Authors: Kaifeng Zhao; Shaofei Wang; Yan Zhang; Thabo Beeler; Siyu Tang
Venue: ECCV 2022 (main)

Abstract: "Synthesizing natural interactions between virtual humans and their 3D environments is critical for numerous applications, such as computer games and AR/VR experiences. Recent methods mainly focus on modeling geometric relations between 3D environments and humans, where the high-level semantics of the human-scene interaction has frequently been ignored. Our goal is to synthesize humans interacting with a given 3D scene controlled by high-level semantic specifications as pairs of action categories and object instances, e.g., “sit on the chair”. The key challenge of incorporating interaction semantics into the generation framework is to learn a joint representation that effectively captures heterogeneous information, including human body articulation, 3D object geometry, and the intent of the interaction. To address this challenge, we design a novel transformer-based generative model, in which the articulated 3D human body surface points and 3D objects are jointly encoded in a unified latent space, and the semantics of the interaction between the human and objects are embedded via positional encoding. Furthermore, inspired by the compositional nature of interactions that humans can simultaneously interact with multiple objects, we define interaction semantics as the composition of varying numbers of atomic action-object pairs. Our proposed generative model can naturally incorporate varying numbers of atomic interactions, which enables synthesizing compositional human-scene interactions without requiring composite interaction data. We extend the PROX dataset with interaction semantic labels and scene instance segmentation to evaluate our method and demonstrate that our method can generate realistic human-scene interactions with semantic control. Our perceptual study shows that our synthesized virtual humans can naturally interact with 3D scenes, considerably outperforming existing methods. We name our method COINS, for COmpositional INteraction Synthesis with Semantic Control. Code and data are available at https://github.com/zkf1997/COINS."
PressureVision: Estimating Hand Pressure from a Single RGB Image
Authors: Patrick Grady; Chengcheng Tang; Samarth Brahmbhatt; Christopher D. Twigg; Chengde Wan; James Hays; Charles C. Kemp
Venue: ECCV 2022 (main)

Abstract: "People often interact with their surroundings by applying pressure with their hands. While hand pressure can be measured by placing pressure sensors between the hand and the environment, doing so can alter contact mechanics, interfere with human tactile perception, require costly sensors, and scale poorly to large environments. We explore the possibility of using a conventional RGB camera to infer hand pressure, enabling machine perception of hand pressure from uninstrumented hands and surfaces. The central insight is that the application of pressure by a hand results in informative appearance changes. Hands share biomechanical properties that result in similar observable phenomena, such as soft-tissue deformation, blood distribution, hand pose, and cast shadows. We collected videos of 36 participants with diverse skin tone applying pressure to an instrumented planar surface. We then trained a deep model (PressureVisionNet) to infer a pressure image from a single RGB image. Our model infers pressure for participants outside of the training data and outperforms baselines. We also show that the output of our model depends on the appearance of the hand and cast shadows near contact regions. Overall, our results suggest the appearance of a previously unobserved human hand can be used to accurately infer applied pressure. Data, code, and models are available online."
PoseScript: 3D Human Poses from Natural Language
Authors: Ginger Delmas; Philippe Weinzaepfel; Thomas Lucas; Francesc Moreno-Noguer; Grégory Rogez
Venue: ECCV 2022 (main)

Abstract: "Natural language is leveraged in many computer vision tasks such as image captioning, cross-modal retrieval or visual question answering, to provide fine-grained semantic information. While human pose is key to human understanding, current 3D human pose datasets lack detailed language descriptions. In this work, we introduce the PoseScript dataset, which pairs a few thousand 3D human poses from AMASS with rich human-annotated descriptions of the body parts and their spatial relationships. To increase the size of this dataset to a scale compatible with typical data hungry learning algorithms, we propose an elaborate captioning process that generates automatic synthetic descriptions in natural language from given 3D keypoints. This process extracts low-level pose information -- the posecodes -- using a set of simple but generic rules on the 3D keypoints. The posecodes are then combined into higher level textual descriptions using syntactic rules. Automatic annotations substantially increase the amount of available data, and make it possible to effectively pretrain deep models for finetuning on human captions. To demonstrate the potential of annotated poses, we show applications of the PoseScript dataset to retrieval of relevant poses from large-scale datasets and to synthetic pose generation, both based on a textual pose description. Code and dataset are available at https://europe.naverlabs.com/research/computer-vision/posescript/."
DProST: Dynamic Projective Spatial Transformer Network for 6D Pose Estimation
Authors: Jaewoo Park; Nam Ik Cho
Venue: ECCV 2022 (main)

Abstract: "Predicting the object’s 6D pose from a single RGB image is a fundamental computer vision task. Generally, the distance between transformed object vertices is employed as an objective function for pose estimation methods. However, projective geometry in the camera space is not considered in those methods and causes performance degradation. In this regard, we propose a new pose estimation system based on a projective grid instead of object vertices. Our pose estimation method, dynamic projective spatial transformer network (DProST), localizes the region of interest grid on the rays in camera space and transforms the grid to object space by estimated pose. The transformed grid is used as both a sampling grid and a new criterion of the estimated pose. Additionally, because DProST does not require object vertices, our method can be used in a mesh-less setting by replacing the mesh with a reconstructed feature. Experimental results show that mesh-less DProST outperforms the state-of-the-art mesh-based methods on the LINEMOD and LINEMOD-OCCLUSION dataset, and shows competitive performance on the YCBV dataset with mesh data. The source code is available at https://github.com/parkjaewoo0611/DProST."
3D Interacting Hand Pose Estimation by Hand De-Occlusion and Removal
Authors: Hao Meng; Sheng Jin; Wentao Liu; Chen Qian; Mengxiang Lin; Wanli Ouyang; Ping Luo
Venue: ECCV 2022 (main)

Abstract: "Estimating 3D interacting hand pose from a single RGB image is essential for understanding human actions. Unlike most previous works that directly predict the 3D poses of two interacting hands simultaneously, we propose to decompose the challenging interacting hand pose estimation task and estimate the pose of each hand separately. In this way, it is straightforward to take advantage of the latest research progress on the single-hand pose estimation system. However, hand pose estimation in interacting scenarios is very challenging, due to (1) severe hand-hand occlusion and (2) ambiguity caused by the homogeneous appearance of hands. To tackle these two challenges, we propose a novel Hand De-occlusion and Removal (HDR) framework to perform hand de-occlusion and distractor removal. We also propose the first large-scale synthetic amodal hand dataset, termed Amodal InterHand Dataset (AIH), to facilitate model training and promote the development of the related research. Experiments show that the proposed method significantly outperforms previous state-of-the-art interacting hand pose estimation approaches. Codes and data are available at https://github.com/MengHao666/HDR."
Pose for Everything: Towards Category-Agnostic Pose Estimation
Authors: Lumin Xu; Sheng Jin; Wang Zeng; Wentao Liu; Chen Qian; Wanli Ouyang; Ping Luo; Xiaogang Wang
Venue: ECCV 2022 (main)

Abstract: "Existing works on 2D pose estimation mainly focus on a certain category, e.g. human, animal, and vehicle. However, there are lots of application scenarios that require detecting the poses/keypoints of the unseen class of objects. In this paper, we introduce the task of Category-Agnostic Pose Estimation (CAPE), which aims to create a pose estimation model capable of detecting the pose of any class of object given only a few samples with keypoint definition. To achieve this goal, we formulate the pose estimation problem as a keypoint matching problem and design a novel CAPE framework, termed POse Matching Network (POMNet). A transformer-based Keypoint Interaction Module (KIM) is proposed to capture both the interactions among different keypoints and the relationship between the support and query images. We also introduce Multi-category Pose (MP-100) dataset, which is a 2D pose dataset of 100 object categories containing over 20K instances and is well-designed for developing CAPE algorithms. Experiments show that our method outperforms other baseline approaches by a large margin. Codes and data are available at https://github.com/luminxu/Pose-for-Everything."
PoseGPT: Quantization-Based 3D Human Motion Generation and Forecasting
Authors: Thomas Lucas; Fabien Baradel; Philippe Weinzaepfel; Grégory Rogez
Venue: ECCV 2022 (main)

Abstract: "We address the problem of action-conditioned generation of human motion sequences. Existing work falls into two categories: forecast models conditioned on observed past motions, or generative models conditioned action labels and duration only. In contrast, we generate motion conditioned on observations of arbitrary length, including none. To solve this generalized problem, we propose PoseGPT, an auto-regressive transformer-based approach which internally compresses human motion into quantized latent sequences. An auto-encoder first maps human motion to latent index sequences in a discrete space, and vice-versa. Inspired by the Generative Pretrained Transformer (GPT), we propose to train a GPT-like model for next-index prediction in that space; this allows PoseGPT to output distributions on possible futures, with or without conditioning on past motion. The discrete and compressed nature of the latent space allows the GPT- like model to focus on long-range signal, as it removes low-level redundancy in the input signal. Predicting discrete indices also alleviates the common pitfall of predicting averaged poses, a typical failure case when regressing continuous values, as the average of discrete targets is not a target itself. Our experimental results show that our proposed approach achieves state-of-the-art results on Hu- manAct12 - a standard but small scale dataset, on BABEL - a recent large scale MoCap dataset and on GRAB - a human-object interactions dataset."
DH-AUG: DH Forward Kinematics Model Driven Augmentation for 3D Human Pose Estimation
Authors: Linzhi Huang; Jiahao Liang; Weihong Deng
Venue: ECCV 2022 (main)

Abstract: "Due to the lack of diversity of datasets, the generalization ability of the pose estimator is poor. To solve this problem, we propose a pose augmentation solution via DH forward kinematics model, which we call DH-AUG. We observe that the previous work is all based on single-frame pose augmentation, if it is directly applied to video pose estimator, there will be several previously ignored problems: (i) angle ambiguity in bone rotation (multiple solutions); (ii) the generated skeleton video lacks movement continuity. To solve these problems, we propose a special generator based on DH forward kinematics model, which is called DH-generator. Extensive experiments demonstrate that DH-AUG can greatly increase the generalization ability of the video pose estimator. In addition, when applied to a single-frame 3D pose estimator, our method outperforms the previous best pose augmentation method. The source code has been released at https://github.com/hlz0606/DH-AUG-DH-Forward-Kinematics-Model-Driven-Augmentation-for-3D-Human-Pose-Estimation."
Estimating Spatially-Varying Lighting in Urban Scenes with Disentangled Representation
Authors: Jiajun Tang; Yongjie Zhu; Haoyu Wang; Jun Hoong Chan; Si Li; Boxin Shi
Venue: ECCV 2022 (main)

Abstract: "We present an end-to-end network for spatially-varying outdoor lighting estimation in urban scenes given a single limited field-of-view LDR image and any assigned 2D pixel position. We use three disentangled latent spaces learned by our network to represent sky light, sun light, and lighting-independent local contents respectively. At inference time, our lighting estimation network can run efficiently in an end-to-end manner by merging the global lighting and the local appearance rendered by the local appearance renderer with the predicted local silhouette. We enhance an existing synthetic dataset with more realistic material models and diverse lighting conditions for more effective training. We also capture the first real dataset with HDR labels for evaluating spatially-varying outdoor lighting estimation. Experiments on both synthetic and real datasets show that our method achieves state-of-the-art performance with more flexible editability."
Boosting Event Stream Super-Resolution with a Recurrent Neural Network
Authors: Wenming Weng; Yueyi Zhang; Zhiwei Xiong
Venue: ECCV 2022 (main)

Abstract: "Existing methods for event stream super-resolution (SR) either require high-quality and high-resolution frames or underperform for large factor SR. To address these problems, we propose a recurrent neural network for event SR without frames. First, we design a temporal propagation net for incorporating neighboring and long-range event-aware contexts that facilitates event SR. Second, we build a spatiotemporal fusion net for reliably aggregating the spatiotemporal clues of event stream. These two elaborate components are tightly synergized for achieving satisfying event SR results even for 16X SR. Synthetic and real-world experimental results demonstrate the clear superiority of our method. Furthermore, we evaluate our method on two downstream event-driven applications, i.e., object recognition and video reconstruction, achieving remarkable performance boost over existing methods."
Projective Parallel Single-Pixel Imaging to Overcome Global Illumination in 3D Structure Light Scanning
Authors: Yuxi Li; Huijie Zhao; Hongzhi Jiang; Xudong Li
Venue: ECCV 2022 (main)

Abstract: "We consider robust and efficient 3D structure light scanning method in situations dominated by global illumination. One typical way of solving this problem is via the analysis of 4D light transport coefficients (LTCs), which contains complete information for a projector-camera pair, and is a 4D data set. However, the process of capturing LTCs generally takes long time. We present projective parallel single-pixel imaging (pPSI), wherein the 4D LTCs are reduced to multiple projection functions to facilitate a highly efficient data capture process. We introduce local maximum constraint, which provides necessary condition for the location of correspondence matching points when projection functions are captured. Local slice extension method is introduced to further accelerate the capture of projection functions. We study the influence of scan ratio in local slice extension method on the accuracy of the correspondence matching points, and conclude that partial scanning is enough for satisfactory results. Our discussions and experiments include three typical kinds of global illuminations: inter-reflections, subsurface scattering, and step edge fringe aliasing. The proposed method is validated in several challenging scenarios."