In this paper, we introduce a method to automatically reconstruct the 3D motion of a person interacting with an object from a single RGB video. Our method estimates the 3D poses of the person and the object, contact positions, and forces and torques actuated by the human limbs. The main contributions of this work are three-fold. First, we introduce an approach to jointly estimate the motion and the actuation forces of the person on the manipulated object by modeling contacts and the dynamics of their interactions. This is cast as a large-scale trajectory optimization problem. Second, we develop a method to automatically recognize from the input video the position and timing of contacts between the person and the object or the ground, thereby significantly simplifying the complexity of the optimization. Third, we validate our approach on a recent MoCap dataset with ground truth contact forces and demonstrate its performance on a new dataset of Internet videos showing people manipulating a variety of tools in unconstrained environments.
involves both recognition of actions and modeling of interactions. In action recognition, most existing approaches that model human-object interactions do not consider 3D, instead model interactions and contacts in the 2D image space @cite_40 @cite_55 @cite_0 @cite_64 . Recent work in scene understanding @cite_23 @cite_24 consider interactions in 3D but have focused on static scene elements rather than manipulated objects as we do in this work. Tracking 3D poses of people interacting with the environment has been demonstrated for bipedal walking @cite_50 @cite_51 or in sports scenarios @cite_60 . However, these works do not consider interactions with objects. Furthermore, @cite_60 requires manual annotation of the input video.
@cite_64 @cite_60 @cite_55 @cite_0 @cite_24 @cite_40 @cite_23 @cite_50 @cite_51
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We introduce an approach for learning human actions as interactions between persons and objects in realistic videos. Previous work typically represents actions with low-level features such as image gradients or optical flow. In contrast, we explicitly localize in space and track over time both the object and the person, and represent an action as the trajectory of the object w.r.t. to the person position. Our approach relies on state-of-the-art techniques for human detection [32], object detection [10], and tracking [39]. We show that this results in human and object tracks of sufficient quality to model and localize human-object interactions in realistic videos. Our human-object interaction features capture the relative trajectory of the object w.r.t. the human. Experimental results on the Coffee and Cigarettes dataset [25], the video dataset of [19], and the Rochester Daily Activities dataset [29] show that 1) our explicit human-object model is an informative cue for action recognition; 2) it is complementary to traditional low-level descriptors such as 3D--HOG [23] extracted over human tracks. We show that combining our human-object interaction features with 3D-HOG improves compared to their individual performance as well as over the state of the art [23], [29]. This paper presents a video-based motion modeling technique for capturing physically realistic human motion from monocular video sequences. We formulate the video-based motion modeling process in an image-based keyframe animation framework. The system first computes camera parameters, human skelet al size, and a small number of 3D key poses from video and then uses 2D image measurements at intermediate frames to automatically calculate the "in between" poses. During reconstruction, we leverage Newtonian physics, contact constraints, and 2D image measurements to simultaneously reconstruct full-body poses, joint torques, and contact forces. We have demonstrated the power and effectiveness of our system by generating a wide variety of physically realistic human actions from uncalibrated monocular video sequences such as sports video footage. We investigate a discriminatively trained model of person-object interactions for recognizing common human actions in still images. We build on the locally order-less spatial pyramid bag-of-features model, which was shown to perform extremely well on a range of object, scene and human action recognition tasks. We introduce three principal contributions. First, we replace the standard quantized local HOG SIFT features with stronger discriminatively trained body part and object detectors. Second, we introduce new person-object interaction features based on spatial co-occurrences of individual body parts and objects. Third, we address the combinatorial problem of a large number of possible interaction pairs and propose a discriminative selection procedure using a linear support vector machine (SVM) with a sparsity inducing regularizer. Learning of action-specific body part and object interactions bypasses the difficult problem of estimating the complete human body pose configuration. Benefits of the proposed model are shown on human action recognition in consumer photographs, outperforming the strong bag-of-features baseline. Detecting objects in cluttered scenes and estimating articulated human body parts from 2D images are two challenging problems in computer vision. The difficulty is particularly pronounced in activities involving human-object interactions (e.g., playing tennis), where the relevant objects tend to be small or only partially visible and the human body parts are often self-occluded. We observe, however, that objects and human poses can serve as mutual context to each other-recognizing one facilitates the recognition of the other. In this paper, we propose a mutual context model to jointly model objects and human poses in human-object interaction activities. In our approach, object detection provides a strong prior for better human pose estimation, while human pose estimation improves the accuracy of detecting the objects that interact with the human. On a six-class sports data set and a 24-class people interacting with musical instruments data set, we show that our mutual context model outperforms state of the art in detecting very difficult objects and estimating human poses, as well as classifying human-object interaction activities. We present an approach which exploits the coupling between human actions and scene geometry to use human pose as a cue for single-view 3D scene understanding. Our method builds upon recent advances in still-image pose estimation to extract functional and geometric constraints on the scene. These constraints are then used to improve single-view 3D scene understanding approaches. The proposed method is validated on monocular time-lapse sequences from YouTube and still images of indoor scenes gathered from the Internet. We demonstrate that observing people performing different actions can significantly improve estimates of 3D scene geometry. Interpretation of images and videos containing humans interacting with different objects is a daunting task. It involves understanding scene or event, analyzing human movements, recognizing manipulable objects, and observing the effect of the human movement on those objects. While each of these perceptual tasks can be conducted independently, recognition rate improves when interactions between them are considered. Motivated by psychological studies of human perception, we present a Bayesian approach which integrates various perceptual tasks involved in understanding human-object interactions. Previous approaches to object and action recognition rely on static shape or appearance feature matching and motion analysis, respectively. Our approach goes beyond these traditional approaches and applies spatial and functional constraints on each of the perceptual elements for coherent semantic interpretation. Such constraints allow us to recognize objects and actions when the appearances are not discriminative enough. We also demonstrate the use of such constraints in recognition of actions from static images without using any motion information. For scene understanding, one popular approach has been to model the object-object relationships. In this paper, we hypothesize that such relationships are only an artifact of certain hidden factors, such as humans. For example, the objects, monitor and keyboard, are strongly spatially correlated only because a human types on the keyboard while watching the monitor. Our goal is to learn this hidden human context (i.e., the human-object relationships), and also use it as a cue for labeling the scenes. We present Infinite Factored Topic Model (IFTM), where we consider a scene as being generated from two types of topics: human configurations and human-object relationships. This enables our algorithm to hallucinate the possible configurations of the humans in the scene parsimoniously. Given only a dataset of scenes containing objects but not humans, we show that our algorithm can recover the human object relationships. We then test our algorithm on the task of attribute and object labeling in 3D scenes and show consistent improvements over the state-of-the-art. Motion and interaction with the environment are fundamentally intertwined. Few people-tracking algorithms exploit such interactions, and those that do assume that surface geometry and dynamics are given. This paper concerns the converse problem, i.e., the inference of contact and environment properties from motion. For 3D human motion, with a 12-segment articulated body model, we show how one can estimate the forces acting on the body in terms of internal forces (joint torques), gravity, and the parameters of a contact model (e.g., the geometry and dynamics of a spring-based model). This is tested on motion capture data and video-based tracking data, with walking, jogging, cartwheels, and jumping.