Generating a rendered image sequence through Monte Carlo ray tracing is an appealing option when one aims to accurately simulate various lighting effects. Unfortunately, interactive rendering scenarios limit the allowable sample size for such sampling-based light transport algorithms, resulting in an unbiased but noisy image sequence. Image denoising has been widely adopted as a post-sampling process to convert such noisy image sequences into biased but temporally stable ones. The state-of-the-art strategy for interactive image denoising involves devising a deep neural network and training this network via supervised learning, i.e., optimizing the network parameters using training datasets that include an extensive set of image pairs (noisy and ground truth images). This paper adopts the prevalent approach for interactive image denoising, which relies on a neural network. However, instead of supervised learning, we propose a different learning strategy that trains our network parameters on the fly, i.e., updating them online using runtime image sequences. To achieve our denoising objective with online learning, we tailor local regression to a cross-regression form that can guide robust training of our denoising neural network. We demonstrate that our denoising framework effectively reduces noise in input image sequences while robustly preserving both geometric and non-geometric edges, without requiring the manual effort involved in preparing an external dataset.

Abstract In avatar‐mediated telepresence systems, a similar environment is assumed for involved spaces, so that the avatar in a remote space can imitate the user's motion with proper semantic intention performed in a local space. For example, touching on the desk by the user should be reproduced by the avatar in the remote space to correctly convey the intended meaning. It is unlikely, however, that the two involved physical spaces are exactly the same in terms of the size of the room or the locations of the placed objects. Therefore, a naive mapping of the user's joint motion to the avatar will not create the semantically correct motion of the avatar in relation to the remote environment. Existing studies have addressed the problem of retargeting human motions to an avatar for telepresence applications. Few studies, however, have focused on retargeting continuous full‐body motions such as locomotion and object interaction motions in a unified manner. In this paper, we propose a novel motion adaptation method that allows to generate the full‐body motions of a human‐like avatar on‐the‐fly in the remote space. The proposed method handles locomotion and object interaction motions as well as smooth transitions between them according to given user actions under the condition of a bijective environment mapping between morphologically‐similar spaces. Our experiments show the effectiveness of the proposed method in generating plausible and semantically correct full‐body motions of an avatar in room‐scale space.

Abstract The processing of captured motion is an essential task for undertaking the synthesis of high‐quality character animation. The motion decomposition techniques investigated in prior work extract meaningful motion primitives that help to facilitate this process. Carefully selected motion primitives can play a major role in various motion‐synthesis tasks, such as interpolation, blending, warping, editing or the generation of new motions. Unfortunately, for a complex character motion, finding generic motion primitives by decomposition is an intractable problem due to the compound nature of the behaviours of such characters. Additionally, decomposed motion primitives tend to be too limited for the chosen model to cover a broad range of motion‐synthesis tasks. To address these challenges, we propose a generative motion decomposition framework in which the decomposed motion primitives are applicable to a wide range of motion‐synthesis tasks. Technically, the input motion is smoothly decomposed into three motion layers. These are base‐level motion, a layer with controllable motion displacements and a layer with high‐frequency residuals. The final motion can easily be synthesized simply by changing a single user parameter that is linked to the layer of controllable motion displacements or by imposing suitable temporal correspondences to the decomposition framework. Our experiments show that this decomposition provides a great deal of flexibility in several motion synthesis scenarios: denoising, style modulation, upsampling and time warping.

Playing with a soccer ball is not easy even for a real human because of dynamic foot contacts with the moving ball while chasing and controlling it. The problem of online full-body soccer motion synthesis is challenging and has not been fully solved yet. In this paper, we present a novel motion control system that produces physically-correct full-body soccer motions: dribbling forward, dribbling to the side, and shooting, in response to an online user motion prescription specified by a motion type, a running speed, and a turning angle. This system performs two tightly-coupled tasks: data-driven motion prediction and physics-based motion synthesis. Given example motion data, the former synthesizes a reference motion in accordance with an online user input and further refines the motion to make the character kick the ball at a right time and place. Provided with the reference motion, the latter then adopts a Model Predictive Control (MPC) framework to generate a physically-correct soccer motion, by solving an optimal control problem that is formulated based on dynamics for a full-body character and the moving ball together with their interactions. Our demonstration shows the effectiveness of the proposed system that synthesizes convincing full-body soccer motions in various scenarios such as adjusting the desired running speed of the character, changing the velocity and the mass of the ball, and maintaining balance against external forces.

Abstract We present a novel approach for synthesizing human gait motions according to a range of input ages by transforming a given motion based on biomechanical observations. Given an original motion, our method first extracts gait cycles that are periodically defined by foot contact on the ground and then transforms the original motion to achieve a desirable posture and motions that respectively correspond to the input age. Among many biomechanical features that gradually change with aging, we mainly focus on spatiotemporal and kinematic features as well as postural changes. Exploiting these features, we formulate the biomechanical changes as continuous functions that reflect visually significant features corresponding to the input age. Finally, we demonstrate that our system can automatically generate plausible gait motions given a wide range of input ages.

Abstract In the paper, we present an online real‐time method for automatically transforming a basic locomotive motion to a desired motion of the same type, based on biomechanical results. Given an online request for a motion of a certain type with desired moving speed and turning angle, our method first extracts a basic motion of the same type from a motion graph, and then transforms it to achieve the desired moving speed and turning angle by exploiting the following biomechanical observations: contact‐driven center‐of‐mass control, anticipatory reorientation of upper body segments, moving speed adjustment, and whole‐body leaning. Exploiting these observations, we propose a simple but effective method to add physical and behavioral naturalness to the resulting locomotive motions without preprocessing. Through experiments, we show that our method enables a character to respond agilely to online user commands while efficiently generating walking, jogging, and running motions with a compact motion library. Our method can also deal with certain dynamical motions such as forward roll. Copyright © 2016 John Wiley & Sons, Ltd.

We present SketchiMo, a novel approach for the expressive editing of articulated character motion. SketchiMo solves for the motion given a set of projective constraints that relate the sketch inputs to the unknown 3 D poses. We introduce the concept of sketch space, a contextual geometric representation of sketch targets---motion properties that are editable via sketch input---that enhances, right on the viewport, different aspects of the motion. The combination of the proposed sketch targets and space allows for seamless editing of a wide range of properties, from simple joint trajectories to local parent-child spatiotemporal relationships and more abstract properties such as coordinated motions. This is made possible by interpreting the user's input through a new sketch-based optimization engine in a uniform way. In addition, our view-dependent sketch space also serves the purpose of disambiguating the user inputs by visualizing their range of effect and transparently defining the necessary constraints to set the temporal boundaries for the optimization.