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Found 23 papers, 2 papers with code
Human motion trajectory prediction using the Social Force Model for real-time and low computational cost applications
Human motion trajectory prediction is a very important functionality for human-robot collaboration, specifically in accompanying, guiding, or approaching tasks, but also in social robotics, self-driving vehicles, or security systems.
A Hierarchical Framework for Collaborative Artificial Intelligence
We propose a hierarchical framework for collaborative intelligent systems.
Robot Navigation Anticipative Strategies in Deep Reinforcement Motion Planning
The navigation of robots in dynamic urban environments, requires elaborated anticipative strategies for the robot to avoid collisions with dynamic objects, like bicycles or pedestrians, and to be human aware.
Weakly and Semi-Supervised Detection, Segmentation and Tracking of Table Grapes with Limited and Noisy Data
To improve detection and segmentation on the target data, we propose to train the segmentation algorithm with a weak bounding box label, while for tracking we leverage 3D Structure from Motion algorithms to generate new labels from already labelled samples.
Humans Social Relationship Classification during Accompaniment
This paper presents the design of deep learning architectures which allow to classify the social relationship existing between two people who are walking in a side-by-side formation into four possible categories --colleagues, couple, family or friendship.
Body Gesture Recognition to Control a Social Robot
In this work, we propose a gesture based language to allow humans to interact with robots using their body in a natural way.
Perception-Intention-Action Cycle in Human-Robot Collaborative Tasks
In this work we argue that in Human-Robot Collaboration (HRC) tasks, the Perception-Action cycle in HRC tasks can not fully explain the collaborative behaviour of the human and robot and it has to be extended to Perception-Intention-Action cycle, where Intention is a key topic.
Single-view 3D Body and Cloth Reconstruction under Complex Poses
We, therefore, propose a coarse-to-fine approach in which we first learn an implicit function that maps the input image to a 3D body shape with a low level of detail, but which correctly fits the underlying human pose, despite its complexity.
Ranked #1 on
3D Reconstruction
on 3DPeople
Permutation-Invariant Relational Network for Multi-person 3D Pose Estimation
For this purpose, we build a residual-like permutation-invariant network that successfully refines potentially corrupted initial 3D poses estimated by an off-the-shelf detector.
3D Multi-Person Pose Estimation (absolute)
3D Multi-Person Pose Estimation (root-relative)
+1
Body Size and Depth Disambiguation in Multi-Person Reconstruction from Single Images
We address the problem of multi-person 3D body pose and shape estimation from a single image.
Effects of a Social Force Model reward in Robot Navigation based on Deep Reinforcement Learning
For this reason, this work adds a dense reward function based on SFM and uses the forces in the states like an additional description.
Improving Map Re-localization with Deep 'Movable' Objects Segmentation on 3D LiDAR Point Clouds
Results show that we are able to accurately re-locate over a filtered map, consistently reducing trajectory errors between an average of 35. 1% with respect to a non-filtered map version and of 47. 9% with respect to a standalone map created on the current session.
3DPeople: Modeling the Geometry of Dressed Humans
Finally, we design a multi-resolution deep generative network that, given an input image of a dressed human, predicts his/her geometry image (and thus the clothed body shape) in an end-to-end manner.
Fast video object segmentation with Spatio-Temporal GANs
To achieve this, we concentrate all the heavy computational load to the training phase with two critics that enforce spatial and temporal mask consistency over the last K frames.
Unsupervised Person Image Synthesis in Arbitrary Poses
Given an input image of a person and a desired pose represented by a 2D skeleton, our model renders the image of the same person under the new pose, synthesizing novel views of the parts visible in the input image and hallucinating those that are not seen.
Geometry-Aware Network for Non-Rigid Shape Prediction from a Single View
We propose a method for predicting the 3D shape of a deformable surface from a single view.
Hallucinating Dense Optical Flow from Sparse Lidar for Autonomous Vehicles
In this paper we propose a novel approach to estimate dense optical flow from sparse lidar data acquired on an autonomous vehicle.
Deep Lidar CNN to Understand the Dynamics of Moving Vehicles
Perception technologies in Autonomous Driving are experiencing their golden age due to the advances in Deep Learning.
Deconvolutional Networks for Point-Cloud Vehicle Detection and Tracking in Driving Scenarios
The system is thoroughly evaluated on the KITTI tracking dataset, and we show the performance boost provided by our CNN-based vehicle detector over a standard geometric approach.
Joint Coarse-And-Fine Reasoning for Deep Optical Flow
We propose a novel representation for dense pixel-wise estimation tasks using CNNs that boosts accuracy and reduces training time, by explicitly exploiting joint coarse-and-fine reasoning.
GANimation: Anatomically-aware Facial Animation from a Single Image
Recent advances in Generative Adversarial Networks (GANs) have shown impressive results for task of facial expression synthesis.
Segmentation-aware Deformable Part Models
In this work we propose a technique to combine bottom-up segmentation, coming in the form of SLIC superpixels, with sliding window detectors, such as Deformable Part Models (DPMs).
Dense Segmentation-Aware Descriptors
In this work we exploit segmentation to construct appearance descriptors that can robustly deal with occlusion and background changes.
