Abstract: This disclosure relates generally to method and system for draping a 3D garment on a 3D human body. Dressing digital humans in 3D have gained much attention due to its use in online shopping and draping 3D garments over the 3D human body has immense applications in virtual try-on, animations, and accurate fitment of the 3D garment is the utmost importance. The proposed disclosure is a single unified garment deformation model that learns the shared space of variations for a body shape, a body pose, and a styling garment. The method receives a plurality of human body inputs to construct a 3D skinned garments for the subject. The deep draper network trained using a plurality of losses provides efficient deep neural network based method that predicts fast and accurate 3D garment images. The method couples the geometric and multi-view perceptual constraints that efficiently learn the garment deformation's high-frequency geometry.

Abstract: The disclosure herein relates to methods and systems for enabling human-robot interaction (HRI) to resolve task ambiguity. Conventional techniques that initiates continuous dialogue with the human to ask a suitable question based on the observed scene until resolving the ambiguity are limited. The present disclosure use the concept of Talk-to-Resolve (TTR) which initiates a continuous dialogue with the user based on visual uncertainty analysis and by asking a suitable question that convey the veracity of the problem to the user and seek guidance until all the ambiguities are resolved. The suitable question is formulated based on the scene understanding and the argument spans present in the natural language instruction. The present disclosure asks questions in a natural way that not only ensures that the user can understand the type of confusion, the robot is facing; but also ensures minimal and relevant questioning to resolve the ambiguities.

Abstract: This disclosure relates generally to a method and system for prediction of correct discrete sensor data, thus enabling continuous flow of data even when a discrete sensor fails. The activities of humans/subjects, housed in a smart environment is continuously monitored by plurality of non-intrusive discrete sensors embedded in living infrastructure. The collected discrete sensor data is usually sparse and largely unbalanced, wherein most of the discrete sensor data is ‘No’ and comparatively only a few samples of ‘Yes’, hence making prediction very challenging. The proposed prediction techniques based on introduction of temporal uncertainty is performed in several stages which includes pre-processing of received discrete sensor data, introduction of temporal uncertainty techniques followed by prediction based on neural network techniques of learning pattern using historical data.

Abstract: This disclosure relates generally to navigation of a tele-robot in dynamic environment using in-situ intelligence. Tele-robotics is the area of robotics concerned with the control of robots (tele-robots) in a remote environment from a distance. In reality the remote environment where the tele robot navigates may be dynamic in nature with unpredictable movements, making the navigation extremely challenging. The disclosure proposes an in-situ intelligent navigation of a tele-robot in a dynamic environment. The disclosed in-situ intelligence enables the tele-robot to understand the dynamic environment by identification and estimation of future location of objects based on a generating/training a motion model. Further the disclosed techniques also enable communication between a master and the tele-robot (whenever necessary) based on an application layer communication semantic.

Abstract: The disclosure herein relates to methods and systems for enabling human-robot interaction (HRI) to resolve task ambiguity. Conventional techniques that initiates continuous dialogue with the human to ask a suitable question based on the observed scene until resolving the ambiguity are limited. The present disclosure use the concept of Talk-to-Resolve (TTR) which initiates a continuous dialogue with the user based on visual uncertainty analysis and by asking a suitable question that convey the veracity of the problem to the user and seek guidance until all the ambiguities are resolved. The suitable question is formulated based on the scene understanding and the argument spans present in the natural language instruction. The present disclosure asks questions in a natural way that not only ensures that the user can understand the type of confusion, the robot is facing; but also ensures minimal and relevant questioning to resolve the ambiguities.

Abstract: Most of the prior art references that generate animations fail to determine and consider head movement data. The prior art references which consider the head movement data for generating the animations rely on a sample video to generate/determine the head movements data, which, as a result, fail to capture changing head motions throughout course of a speech given by a subject in an actual whole length video. The disclosure herein generally relates to generating facial animations, and, more particularly, to a method and system for generating the facial animations from speech signal of a subject. The system determines the head movement, lip movements, and eyeball movements, of the subject, by processing a speech signal collected as input, and uses the head movement, lip movements, and eyeball movements, to generate an animation.

Abstract: The present disclosure provides a system and a method for stitching images using non-linear optimization and multi-constraint cost function minimization. Most of conventional homography based transformation approaches for image alignment, calculate transformations based on linear algorithms which ignore parameters such as lens distortion and unable to handle parallax for non-planar images resulting in improper image stitching with misalignments. The disclosed system and the method generates initial stitched image by estimating a global homography for each image using estimated pairwise homography matrix and feature point correspondences for each pair of images, based on a non-linear optimization. Local warping based image alignment is applied on the initial stitched image, using multi-constraint cost function minimization to mitigate aberrations caused by noises in the global homography estimation to generate the refined stitched image.

Abstract: This disclosure relates generally to system and method for forecasting location of target in monocular first person view. Conventional systems for location forecasting utilizes complex neural networks and hence are computationally intensive and requires high compute power. The disclosed system includes an efficient and light-weight RNN based network model for predicting motion of targets in first person monocular videos. The network model includes an auto-encoder in the encoding phase and a regularizing layer in the end helps us get better accuracy. The disclosed method relies entirely just on detection bounding boxes for prediction as well as training of the network model and is still capable of transferring zero-shot on a different dataset.

Abstract: This disclosure relates generally to a method and system for generating 2D animated lip images synchronizing to an audio signal for an unseen subject. Recent advances in Convolutional Neural Network (CNN) based approaches generate convincing talking heads. Personalization of such talking heads requires training the model with large number of samples of the target person which is time consuming. The lip generator system receives an audio signal and a target lip image of an unseen target subject as inputs from a user and processes these inputs to extract a plurality of high dimensional audio image features. The lip generator system is meta-trained with training dataset which consists of large variety of subjects' ethnicity and vocabulary. The meta-trained model generates realistic animation for previously unseen face and unseen audio when finetuned with only a few-shot samples for a predefined interval of time. Additionally, the method protects intrinsic features of the unseen target subject.

Abstract: Estimating 3D human pose from monocular images is a challenging problem due to the variety and complexity of human poses and the inherent ambiguity in recovering depth from single view. Recent deep learning based methods show promising results by using supervised learning on 3D pose annotated datasets. However, the lack of large-scale 3D annotated training data makes the 3D pose estimation difficult in-the-wild. Embodiments of the present disclosure provide a method which can effectively predict 3D human poses from only 2D pose in a weakly-supervised manner by using both ground-truth 3D pose and ground-truth 2D pose based on re-projection error minimization as a constraint to predict the 3D joint locations. The method may further utilize additional geometric constraints on reconstructed body parts to regularize the pose in 3D along with minimizing re-projection error to improvise on estimating an accurate 3D pose.

Abstract: Conventional state-of-the-art methods are limited in their ability to generate realistic animation from audio on any unknown faces and cannot be easily generalized to different facial characteristics and voice accents. Further, these methods fail to produce realistic facial animation for subjects which are quite different than that of distribution of facial characteristics network has seen during training. Embodiments of the present disclosure provide systems and methods that generate audio-speech driven animated talking face using a cascaded generative adversarial network (CGAN), wherein a first GAN is used to transfer lip motion from canonical face to person-specific face. A second GAN based texture generator network is conditioned on person-specific landmark to generate high-fidelity face corresponding to the motion. Texture generator GAN is made more flexible using meta learning to adapt to unknown subject's traits and orientation of face during inference.

Abstract: This disclosure relates to systems and methods for solving generic inverse problems by providing a coupled representation architecture using transform learning. Convention solutions are complex, require long training and testing times, reconstruction quality also may not be suitable for all applications. Furthermore, they preclude application to real-time scenarios due to the mentioned inherent lacunae. The methods provided herein require involve very low computational complexity with a need for only three matrix-vector products, and requires very short training and testing times, which makes it applicable for real-time applications. Unlike the conventional learning architectures using inductive approaches, the CASC of the present disclosure can learn directly from the source domain and the number of features in a source domain may not be necessarily equal to the number of features in a target domain.

Abstract: The disclosure generally relates to methods and systems for enabling human robot interaction by cognition sharing which includes gesture and audio. Conventional techniques that use the gestures and the speech, require extra hardware setup and are limited to navigation in structured outdoor driving environments. The present disclosure herein provides methods and systems that solves the technical problem of enabling the human robot interaction with a two-step approach by transferring the cognitive load from the human to the robot. An accurate shared perspective associated with the task is determined in the first step by computing relative frame transformations based on understanding of navigational gestures of the subject. Then, the shared perspective transformed to the robot in the field view of the robot. The transformed shared perspective is then given to a language grounding technique in the second step, to accurately determine a final goal associated with the task.

Abstract: Speech-driven facial animation is useful for a variety of applications such as telepresence, chatbots, etc. The necessary attributes of having a realistic face animation are: 1) audiovisual synchronization, (2) identity preservation of the target individual, (3) plausible mouth movements, and (4) presence of natural eye blinks. Existing methods mostly address audio-visual lip synchronization, and synthesis of natural facial gestures for overall video realism. However, existing approaches are not accurate. Present disclosure provides system and method that learn motion of facial landmarks as an intermediate step before generating texture. Person-independent facial landmarks are generated from audio for invariance to different voices, accents, etc. Eye blinks are imposed on facial landmarks and the person-independent landmarks are retargeted to person-specific landmarks to preserve identity related facial structure.

Abstract: Speech-driven facial animation is useful for a variety of applications such as telepresence, chatbots, etc. The necessary attributes of having a realistic face animation are: 1) audiovisual synchronization, (2) identity preservation of the target individual, (3) plausible mouth movements, and (4) presence of natural eye blinks. Existing methods mostly address audio-visual lip synchronization, and synthesis of natural facial gestures for overall video realism. However, existing approaches are not accurate. Present disclosure provides system and method that learn motion of facial landmarks as an intermediate step before generating texture. Person-independent facial landmarks are generated from audio for invariance to different voices, accents, etc. Eye blinks are imposed on facial landmarks and the person-independent landmarks are retargeted to person-specific landmarks to preserve identity related facial structure.

Abstract: Conventional tele-presence robots have their own limitations with respect to task execution, information processing and management. Embodiments of the present disclosure provide a tele-presence robot (TPR) that communicates with a master device associated with a user via an edge device for task execution wherein control command from the master device is parsed for determining instructions set and task type for execution. Based on this determination, the TPR queries for information across storage devices until a response is obtained enough to execute task. The task upon execution is validated with the master device and user. Knowledge acquired, during querying, task execution and validation of the executed task, is dynamically partitioned by the TPR across storage devices namely, on-board memory of the tele-present robot, an edge device, a cloud and a web interface respectively depending upon the task type, operating environment of the tele-presence robot, and other performance affecting parameters.

Abstract: Robotic platform for tele-presence applications has gained paramount importance, such as for remote meetings, group discussions, and the like and has sought much attention. There exist some robotic platforms for such tele-presence applications, these lack efficacy in communication and interaction between remote person and avatar robot deployed in another geographic location thus adding network overhead. Embodiments of the present disclosure for edge centric communication protocol for remotely maneuvering tele-presence robot in geographically distributed environment.

Abstract: A method and system is provided for finding and analyzing gait parameters and postural balance of a person using a Kinect system. The system is easy to use and can be installed at home as well as in clinic. The system includes a Kinect sensor, a software development kit (SDK) and a processor. The temporal skeleton information obtained from the Kinect sensor to evaluate gait parameters includes stride length, stride time, stance time and swing time. Eigenvector based curvature detection is used to analyze the gait pattern with different speeds. In another embodiment, Eigenvector based curvature detection is employed to detect static single limb stance (SLS) duration along with gait variables for evaluating body balance.

Abstract: Body joint tracking is applied in various industries and medical field. In body joint tracking, marker less devices plays an important role. However, the marker less devices are facing some challenges in providing optimal tracking due to occlusion, ambiguity, lighting conditions, dynamic objects etc. System and method of the present disclosure provides an optimized body joint tracking. Here, motion data pertaining to a first set of motion frames from a motion sensor are received. Further, the motion data are processed to obtain a plurality of 3 dimensional cylindrical models. Here, every cylindrical model among the plurality of 3 dimensional cylindrical model represents a body segment. The coefficients associated with the plurality of 3 dimensional cylindrical models are initialized to obtain a set of initialized cylindrical models. A set of dynamic coefficients associated with the initialized cylindrical models are utilized to track joint motion trajectories of a set of subsequent frames.

Abstract: The embodiments herein provide a system and method for integrating objects in monocular simultaneous localization and mapping (SLAM). State of art object SLAM approach use two popular threads. In first, instance specific models are assumed to be known a priori. In second, a general model for an object such as ellipsoids and cuboids is used. However, these generic models just give the label of the object category and do not give much information about the object pose in the map. The method and system disclosed provide a SLAM framework on a real monocular sequence wherein joint optimization is performed on object localization and edges using category level shape priors and bundle adjustment. The method provides a better visualization incorporating object representations in the scene along with the 3D structure of the base SLAM system, which makes it useful for augmented reality (AR) applications.