We propose SMPLitex, a method for estimating and manipulating the complete 3D appearance of humans captured from a single image.
We propose a method to estimate the mechanical parameters of fabrics using a casual capture setup with a depth camera.
Through this, we demonstrate the quality of our probabilistic reconstruction and show that explicit ambiguity modeling is better-suited for this challenging problem.
Intrinsic imaging or intrinsic image decomposition has traditionally been described as the problem of decomposing an image into two layers: a reflectance, the albedo invariant color of the material; and a shading, produced by the interaction between light and geometry.
Moreover, we demonstrate that our approach offers previously unseen two-hand tracking performance from RGB, and quantitatively and qualitatively outperforms existing RGB-based methods that were not explicitly designed for two-hand interactions.
We present a novel method for real-time pose and shape reconstruction of two strongly interacting hands.
We propose a new generative model for 3D garment deformations that enables us to learn, for the first time, a data-driven method for virtual try-on that effectively addresses garment-body collisions.
Then, after a mesh topology optimization step where we generate a sufficient level of detail for the input garment type, we further deform the mesh to reproduce deformations caused by the target body shape.
We present SoftSMPL, a learning-based method to model realistic soft-tissue dynamics as a function of body shape and motion.
We propose a model that separates global garment fit, due to body shape, from local garment wrinkles, due to both pose dynamics and body shape.
The estimation of the optical properties of a material from RGB-images is an important but extremely ill-posed problem in Computer Graphics.
We address the highly challenging problem of real-time 3D hand tracking based on a monocular RGB-only sequence.
A real-time kinematic skeleton fitting method uses the CNN output to yield temporally stable 3D global pose reconstructions on the basis of a coherent kinematic skeleton.
Ranked #16 on Pose Estimation on Leeds Sports Poses
We present an approach for real-time, robust and accurate hand pose estimation from moving egocentric RGB-D cameras in cluttered real environments.
Marker-based and marker-less optical skeletal motion-capture methods use an outside-in arrangement of cameras placed around a scene, with viewpoints converging on the center.
We propose a CNN-based approach for 3D human body pose estimation from single RGB images that addresses the issue of limited generalizability of models trained solely on the starkly limited publicly available 3D pose data.
Ranked #17 on Pose Estimation on Leeds Sports Poses
We propose a new model-based method to accurately reconstruct human performances captured outdoors in a multi-camera setup.
However, due to difficult occlusions, fast motions, and uniform hand appearance, jointly tracking hand and object pose is more challenging than tracking either of the two separately.
We therefore propose a new method for real-time, marker-less and egocentric motion capture which estimates the full-body skeleton pose from a lightweight stereo pair of fisheye cameras that are attached to a helmet or virtual reality headset.
Our method uses a new image formation model with analytic visibility and analytically differentiable alignment energy.