Abstract Motion in‐betweening has emerged as a promising approach to enhance the efficiency of motion creation due to its flexibility and time performance. However, previous in‐betweening methods are limited to generating short transitions due to growing pose ambiguity when the number of missing frames increases. This length‐related constraint makes the optimization hard and it further causes another constraint on the target pose, limiting the degrees of freedom for artists to use. In this paper, we introduce a keyframe‐driven approach that effectively solves the pose ambiguity problem, allowing robust in‐betweening performance on various lengths of missing frames. To incorporate keyframe‐driven motion synthesis, we introduce a keyframe score that measures the likelihood of a frame being used as a keyframe as well as an adaptive keyframe selection method that maintains appropriate temporal distances between resulting keyframes. Additionally, we employ phase manifolds to further resolve the pose ambiguity and incorporate trajectory conditions to guide the approximate movement of the character. Comprehensive evaluations, encompassing both quantitative and qualitative analyses, were conducted to compare our method with state‐of‐the‐art in‐betweening approaches across various transition lengths. The code for the paper is available at https://github.com/seokhyeonhong/long-mib

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 Stitching different character motions is one of the most commonly used techniques as it allows the user to make new animations that fit one's purpose from pieces of motion. However, current motion stitching methods often produce unnatural motion with foot sliding artefacts, depending on the performance of the interpolation. In this paper, we propose a novel motion stitching technique based on a recurrent motion refiner (RMR) that connects discontinuous locomotions into a single natural locomotion. Our model receives different locomotions as input, in which the root of the last pose of the previous motion and that of the first pose of the next motion are aligned. During runtime, the model slides through the sequence, editing frames window by window to output a smoothly connected animation. Our model consists of a two‐layer recurrent network that comes between a simple encoder and decoder. To train this network, we created a sufficient number of paired data with a newly designed data generation. This process employs a K‐nearest neighbour search that explores a predefined motion database to create the corresponding input to the ground truth. Once trained, the suggested model can connect various lengths of locomotion sequences into a single natural locomotion.

StageMix is a mixed video that is created by concatenating the segments from various performance videos of an identical song in a visually smooth manner by matching the main subject's silhouette presented in the frame. We introduce PopStage , which allows users to generate a StageMix automatically. PopStage is designed based on the StageMix Editing Guideline that we established by interviewing creators as well as observing their workflows. PopStage consists of two main steps: finding an editing path and generating a transition effect at a transition point. Using a reward function that favors visual connection and the optimality of transition timing across the videos, we obtain the optimal path that maximizes the sum of rewards through dynamic programming. Given the optimal path, PopStage then aligns the silhouettes of the main subject from the transitioning video pair to enhance the visual connection at the transition point. The virtual camera view is next optimized to remove the black areas that are often created due to the transformation needed for silhouette alignment, while reducing pixel loss. In this process, we enforce the view to be the maximum size while maintaining the temporal continuity across the frames. Experimental results show that PopStage can generate a StageMix of a similar quality to those produced by professional creators in a highly reduced production time.

Reinforcement learning (RL) has been proven effective in many scenarios, including environment exploration and motion planning. However, its application in data-driven character control has produced relatively simple motion results compared to recent approaches that have used large complex motion data without RL. In this paper, we provide a real-time motion control method that can generate high-quality and complex motion results from various sets of unstructured data while retaining the advantage of using RL, which is the discovery of optimal behaviors by trial and error. We demonstrate the results for a character achieving different tasks, from simple direction control to complex avoidance of moving obstacles. Our system works equally well on biped/quadruped characters, with motion data ranging from 1 to 48 minutes, without any manual intervention. To achieve this, we exploit a finite set of discrete actions, where each action represents full-body future motion features. We first define a subset of actions that can be selected in each state and store these pieces of information in databases during the preprocessing step. The use of this subset of actions enables the effective learning of control policy even from a large set of motion data. To achieve interactive performance at run-time, we adopt a proposal network and a k-nearest neighbor action sampler.

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.

We propose a novel motion retargeting method that efficiently estimates artist-friendly rig space parameters. Inspired by the workflow typically observed in keyframe animation, our approach transfers a source motion into a production friendly character rig by optimizing the rig space parameters while balancing the considerations of fidelity to the source motion and the ease of subsequent editing. We propose the use of an intermediate object to transfer both the skeletal motion and the mesh deformation. The target rig-space parameters are then optimized to minimize the error between the motion of an intermediate object and the target character. The optimization uses a set of artist defined weights to modulate the effect of the different rig space parameters over time. Sparsity inducing regularizers and keyframe extraction streamline any additional editing processes. The results obtained with different types of character rigs demonstrate the versatility of our method and its effectiveness in simplifying any necessary manual editing within the production pipeline.

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.

In upcoming ubiquitous environment, many real-time sensing applications will emerge. These applications show different scale and characteristics on delivery demands. The applications commonly depend on real-time understanding on data from widely distributed data sources. Also, they have highly diverse and complex delivery demands in terms of data and delay, e.g., data value ranges of interest, spatial and temporal resolution and tolerable delay, etc. Due to the remarkable scale and complexity, however, existing data delivery schemes cannot support the applications effectively. We present a novel data delivery scheme to support real-time sensing applications. Our scheme provides efficient delivery paths over Internet-based delivery infrastructure, which is comprised of a collection of dispersed nodes forming an overlay network. It fully exploits the diversity of delivery demands on both data and delay requirements, thus achieving high level of service satisfaction and efficiency at the same time. Also, it distributes path management overhead to multiple nodes by adopting a hierarchical path management approach. We evaluate our scheme through a large-scale simulation. The results show that it achieves a high level of scalability and bandwidth efficiency.