13 research outputs found
Implicit Neural Head Synthesis via Controllable Local Deformation Fields
High-quality reconstruction of controllable 3D head avatars from 2D videos is
highly desirable for virtual human applications in movies, games, and
telepresence. Neural implicit fields provide a powerful representation to model
3D head avatars with personalized shape, expressions, and facial parts, e.g.,
hair and mouth interior, that go beyond the linear 3D morphable model (3DMM).
However, existing methods do not model faces with fine-scale facial features,
or local control of facial parts that extrapolate asymmetric expressions from
monocular videos. Further, most condition only on 3DMM parameters with poor(er)
locality, and resolve local features with a global neural field. We build on
part-based implicit shape models that decompose a global deformation field into
local ones. Our novel formulation models multiple implicit deformation fields
with local semantic rig-like control via 3DMM-based parameters, and
representative facial landmarks. Further, we propose a local control loss and
attention mask mechanism that promote sparsity of each learned deformation
field. Our formulation renders sharper locally controllable nonlinear
deformations than previous implicit monocular approaches, especially mouth
interior, asymmetric expressions, and facial details.Comment: Accepted at CVPR 202
FML: Face Model Learning from Videos
Monocular image-based 3D reconstruction of faces is a long-standing problem
in computer vision. Since image data is a 2D projection of a 3D face, the
resulting depth ambiguity makes the problem ill-posed. Most existing methods
rely on data-driven priors that are built from limited 3D face scans. In
contrast, we propose multi-frame video-based self-supervised training of a deep
network that (i) learns a face identity model both in shape and appearance
while (ii) jointly learning to reconstruct 3D faces. Our face model is learned
using only corpora of in-the-wild video clips collected from the Internet. This
virtually endless source of training data enables learning of a highly general
3D face model. In order to achieve this, we propose a novel multi-frame
consistency loss that ensures consistent shape and appearance across multiple
frames of a subject's face, thus minimizing depth ambiguity. At test time we
can use an arbitrary number of frames, so that we can perform both monocular as
well as multi-frame reconstruction.Comment: CVPR 2019 (Oral). Video: https://www.youtube.com/watch?v=SG2BwxCw0lQ,
Project Page: https://gvv.mpi-inf.mpg.de/projects/FML19
A Data-Driven Approach for Real-Time Full Body Pose Reconstruction from a Depth Camera
In recent years, depth cameras have become a widely available sensor type that captures depth images at realtime frame rates. Even though recent approaches have shown that 3D pose estimation from monocular 2.5D depth images has become feasible, there are still challenging problems due to strong noise in the depth data and selfocclusions in the motions being captured. In this paper, we present an efficient and robust pose estimation framework for tracking full-body motions from a single depth image stream. Following a data-driven hybrid strategy that combines local optimization with global retrieval techniques, we contribute several technical improvements that lead to speed-ups of an order of magnitude compared to previous approaches. In particular, we introduce a variant of Dijkstra’s algorithm to efficiently extract pose features from the depth data and describe a novel late-fusion scheme based on an efficiently computable sparse Hausdorff distance to combine local and global pose estimates. Our experiments show that the combination of these techniques facilitates real-time tracking with stable results even for fast and complex motions, making it applicable to a wide range of interactive scenarios. 1
Computational design of walking automata
Creating mechanical automata that can walk in stable and pleasing manners is a challenging task that requires both skill and expertise. We propose to use computational design to offset the technical difficulties of this process. A simple drag-and-drop interface allows casual users to create personalized walking toys from a library of pre-defined template mechanisms. Provided with this input, our method leverages physical simulation and evolutionary optimization to refine the mechanical designs such that the resulting toys are able to walk. The optimization process is guided by an intuitive set of objectives that measure the quality of the walking motions. We demonstrate our approach on a set of simulated mechanical toys with different numbers of legs and various distinct gaits. Two fabricated prototypes showcase the feasibility of our designs
PersonalizationandEvaluationofaReal-timeDepth-basedFullBodyTracker
Reconstructing a three-dimensional representation of human motion in real-time constitutes an important research topic with applications in sports sciences, humancomputer-interaction,andthemovieindustry.Inthispaper, we contribute with a robust algorithm for estimating a personalized human body model from just two sequentially captured depth images that is more accurate and runs an order of magnitude faster than the current state-ofthe-art procedure. Then, we employ the estimated body model to track the pose in real-time from a stream of depth images using a tracking algorithm that combines local pose optimization and a stabilizing database lookup. Together, this enables accurate pose tracking that is more accurate than previous approaches. As a further contribution, we evaluate and compare our algorithm t