Nils Hasler

Markerless Motion Capture with unsynchronized moving cameras

In this work we present an approach for markerless motion capture (MoCap) of articulated objects, which are recorded with multiple unsynchronized moving cameras. Instead of using fixed (and expensive) hardware synchronized cameras, this approach allows us to track people with off-the-shelf handheld video cameras. To prepare a sequence for motion capture, we first reconstruct the static background and the position of each camera using Structure-from-Motion (SfM). Then the cameras are registered to each other using the reconstructed static background geometry. Camera synchronization is achieved via the audio streams recorded by the cameras in parallel. Finally, a markerless MoCap approach is applied to recover positions and joint configurations of subjects. Feature tracks and dense background geometry are further used to stabilize the MoCap. The experiments show examples with highly challenging indoor and outdoor scenes.

Fast articulated motion tracking using a sums of Gaussians body model

We present an approach for modeling the human body by Sums of spatial Gaussians (SoG), allowing us to perform fast and high-quality markerless motion capture from multi-view video sequences. The SoG model is equipped with a color model to represent the shape and appearance of the human and can be reconstructed from a sparse set of images. Similar to the human body, we also represent the image domain as SoG that models color consistent image blobs. Based on the SoG models of the image and the human body, we introduce a novel continuous and differentiable model-to-image similarity measure that can be used to estimate the skeletal motion of a human at 5–15 frames per second even for many camera views. In our experiments, we show that our method, which does not rely on silhouettes or training data, offers an good balance between accuracy and computational cost.

Technical Section: Estimating body shape of dressed humans

The paper presents a method to estimate the detailed 3D body shape of a person even if heavy or loose clothing is worn. The approach is based on a space of human shapes, learned from a large database of registered body scans. Together with this database we use as input a 3D scan or model of the person wearing clothes and apply a fitting method, based on ICP (iterated closest point) registration and Laplacian mesh deformation. The statistical model of human body shapes enforces that the model stays within the space of human shapes. The method therefore allows us to compute the most likely shape and pose of the subject, even if it is heavily occluded or body parts are not visible. Several experiments demonstrate the applicability and accuracy of our approach to recover occluded or missing body parts from 3D laser scans.

Multilinear pose and body shape estimation of dressed subjects from image sets

In this paper we propose a multilinear model of human pose and body shape which is estimated from a database of registered 3D body scans in different poses. The model is generated by factorizing the measurements into pose and shape dependent components. By combining it with an ICP based registration method, we are able to estimate pose and body shape of dressed subjects from single images. If several images of the subject are available, shape and poses can be optimized simultaneously for all input images. Additionally, while estimating pose and shape, we use the model as a virtual calibration pattern and also recover the parameters of the perspective camera model the images were created with.

Performance capture of interacting characters with handheld kinects

We present an algorithm for marker-less performance capture of interacting humans using only three hand-held Kinect cameras. Our method reconstructs human skeletal poses, deforming surface geometry and camera poses for every time step of the depth video. Skeletal configurations and camera poses are found by solving a joint energy minimization problem which optimizes the alignment of RGBZ data from all cameras, as well as the alignment of human shape templates to the Kinect data. The energy function is based on a combination of geometric correspondence finding, implicit scene segmentation, and correspondence finding using image features. Only the combination of geometric and photometric correspondences and the integration of human pose and camera pose estimation enables reliable performance capture with only three sensors. As opposed to previous performance capture methods, our algorithm succeeds on general uncontrolled indoor scenes with potentially dynamic background, and it succeeds even if the cameras are moving.

Capture and Statistical Modeling of Arm-Muscle Deformations

We present a comprehensive data‐driven statistical model for skin and muscle deformation of the human shoulder‐arm complex. Skin deformations arise from complex bio‐physical effects such as non‐linear elasticity of muscles, fat, and connective tissue; and vary with physiological constitution of the subjects and external forces applied during motion. Thus, they are hard to model by direct physical simulation. Our alternative approach is based on learning deformations from multiple subjects performing different exercises under varying external forces. We capture the training data through a novel multi‐camera approach that is able to reconstruct fine‐scale muscle detail in motion. The resulting reconstructions from several people are aligned into one common shape parametrization, and learned using a semi‐parametric non‐linear method. Our learned data‐driven model is fast, compact and controllable with a small set of intuitive parameters – pose, body shape and external forces, through which a novice artist can interactively produce complex muscle deformations. Our method is able to capture and synthesize fine‐scale muscle bulge effects to a greater level of realism than achieved previously. We provide quantitative and qualitative validation of our method.

Learning skeletons for shape and pose

In this paper a method for estimating a rigid skeleton, including skinning weights, skeleton connectivity, and joint positions, given a sparse set of example poses is presented. In contrast to other methods, we are able to simultaneously take examples of different subjects into account, which improves the robustness of the estimation. It is additionally possible to generate a skeleton that primarily describes variations in body shape instead of pose. The shape skeleton can then be combined with a regular pose varying skeleton. That way pose and body shape can be controlled simultaneously but separately. As this skeleton is technically still just a skinned rigid skeleton, compatibility with major modelling packages and game engines is retained. We further present an approach for synthesizing a suitable bind shape that additionally improves the accuracy of the generated model.

Automatic Track Recognition of Footprints for Identifying Cryptic Species

The recognition of tracks plays an important role in ecological research and monitoring, and tracking tunnels are a cost-effective method for indexing species over large areas. Traditionally, tracks are collected by a tracking system, and analysis is cairied out in a manual identification procedure by experienced wildlife biologists. Unfortunately, human experts are unable to reliably distinguish tracks of morphologically similar species. We propose a new method using image analysis, which allows automatic species identification of tracks, and apply the method to identifying cryptic small-mammal species. We demonstrate the method by identifying footprints of three invasive rat species with similar morphology that co-occur in New Zealand, including detection of a recent invasion of a rat-free island. Automatic footprint recognition successfully identified the species of rat for >70% of footprints, and >83% of tracking cards. With appropriate changes to the image recognition, the method could be broadly applicable to any taxa that can be tracked. Identification of tracks to species level gives better estimates of species presence and composition in communities.

Spatio-temporal motion tracking with unsynchronized cameras

We present a new spatio-temporal method for markerless motion capture. We reconstruct the pose and motion of a character from a multi-view video sequence without requiring the cameras to be synchronized and without aligning captured frames in time. By formulating the model-to-image similarity measure as a temporally continuous functional, we are also able to reconstruct motion in much higher temporal detail than was possible with previous synchronized approaches. By purposefully running cameras unsynchronized we can capture even very fast motion at speeds that off-the-shelf but high quality cameras provide.

Free-Viewpoint Video of Human Actors Using Multiple Handheld Kinects

We present an algorithm for creating free-viewpoint video of interacting humans using three handheld Kinect cameras. Our method reconstructs deforming surface geometry and temporal varying texture of humans through estimation of human poses and camera poses for every time step of the RGBZ video. Skeletal configurations and camera poses are found by solving a joint energy minimization problem, which optimizes the alignment of RGBZ data from all cameras, as well as the alignment of human shape templates to the Kinect data. The energy function is based on a combination of geometric correspondence finding, implicit scene segmentation, and correspondence finding using image features. Finally, texture recovery is achieved through jointly optimization on spatio-temporal RGB data using matrix completion. As opposed to previous methods, our algorithm succeeds on free-viewpoint video of human actors under general uncontrolled indoor scenes with potentially dynamic background, and it succeeds even if the cameras are moving.