HeadOn: Real-time Reenactment of Human Portrait Videos

We propose HeadOn, the first real-time source-to-target reenactment approach for complete human portrait videos that enables transfer of torso and head motion, face expression, and eye gaze. Given a short RGB-D video of the target actor, we automatically construct a personalized geometry proxy that embeds a parametric head, eye, and kinematic torso model. A novel real-time reenactment algorithm employs this proxy to photo-realistically map the captured motion from the source actor to the target actor. On top of the coarse geometric proxy, we propose a video-based rendering technique that composites the modified target portrait video via view- and pose-dependent texturing, and creates photo-realistic imagery of the target actor under novel torso and head poses, facial expressions, and gaze directions. To this end, we propose a robust tracking of the face and torso of the source actor. We extensively evaluate our approach and show significant improvements in enabling much greater flexibility in creating realistic reenacted output videos.Comment: Video: https://www.youtube.com/watch?v=7Dg49wv2c_g Presented at Siggraph'1

Real Time Animation of Virtual Humans: A Trade-off Between Naturalness and Control

Virtual humans are employed in many interactive applications using 3D virtual environments, including (serious) games. The motion of such virtual humans should look realistic (or ‘natural’) and allow interaction with the surroundings and other (virtual) humans. Current animation techniques differ in the trade-off they offer between motion naturalness and the control that can be exerted over the motion. We show mechanisms to parametrize, combine (on different body parts) and concatenate motions generated by different animation techniques. We discuss several aspects of motion naturalness and show how it can be evaluated. We conclude by showing the promise of combinations of different animation paradigms to enhance both naturalness and control

LEARNING TO RIG CHARACTERS

With the emergence of 3D virtual worlds, 3D social media, and massive online games, the need for diverse, high-quality, animation-ready characters and avatars is greater than ever. To animate characters, artists hand-craft articulation structures, such as animation skeletons and part deformers, which require significant amount of manual and laborious interaction with 2D/3D modeling interfaces. This thesis presents deep learning methods that are able to significantly automate the process of character rigging. First, the thesis introduces RigNet, a method capable of predicting an animation skeleton for an input static 3D shape in the form of a polygon mesh. The predicted skeletons match the animator expectations in joint placement and topology. RigNet also estimates surface skin weights which determine how the mesh is animated given the different skeletal poses. In contrast to prior work that fits pre-defined skeletal templates with hand-tuned objectives, RigNet is able to automatically rig diverse characters, such as humanoids, quadrupeds, toys, birds, with varying articulation structure and geometry. RigNet is based on a deep neural architecture that directly operates on the mesh representation. The architecture is trained on a diverse dataset of rigged models that we mined online and curated. The dataset includes 2.7K polygon meshes, along with their associated skeletons and corresponding skin weights. Second, the thesis introduces Morig, a method that automatically rigs character meshes driven by single-view point cloud streams capturing the motion of performing characters. Compared to RigNet, MoRig\u27s rigging is \emph{motion-aware}: its neural network encodes motion cues from the point clouds into compact feature representations that are informative about the articulated parts of the performing character. These motion-aware features guide the inference of an appropriate skeletal rig for the input mesh. Furthermore, Morig is able to animate the rig according to the captured point cloud motion. Morig can handle diverse characters with different morphologies (e.g., humanoids, quadrupeds, toy characters). It also accounts for occluded regions in the point clouds and mismatches in the part proportions between the input mesh and captured character. Third, the thesis introduces APES, a method that takes as input 2D raster images depicting a small set of poses of a character shown in a sprite sheet, and identifies articulated parts useful for rigging the character. APES uses a combination of neural network inference and integer linear programming to identify a compact set of articulated body parts, e.g. head, torso and limbs, that best reconstruct the input poses. Compared to Morig and RigNet that require a large collection of training models with associated skeletons and skinning weights, APES\u27 neural architecture relies on less effortful supervision from (i) pixel correspondences readily available in existing large cartoon image datasets (e.g., Creative Flow), (ii) a relatively small dataset of 57 cartoon characters segmented into moving parts. Finally, the thesis discusses future research directions related to combining neural rigging with 3D and 4D reconstruction of characters from point cloud data and 2D video as well as automating the process of motion synthesis for 3D characters

Automatic generation of dynamic skin deformation for animated characters

© 2018 by the authors. Since non-automatic rigging requires heavy human involvements, and various automatic rigging algorithms are less efficient in terms of computational efficiency, especially for current curve-based skin deformation methods, identifying the iso-parametric curves and creating the animation skeleton requires tedious and time-consuming manual work. Although several automatic rigging methods have been developed, but they do not aim at curve-based models. To tackle this issue, this paper proposes a new rigging algorithm for automatic generation of dynamic skin deformation to quickly identify iso-parametric curves and create an animation skeleton in a few milliseconds, which can be seamlessly used in curve-based skin deformation methods to make the rigging process fast enough for highly efficient computer animation applications