Learning motion manifolds with convolutional autoencoders

193

103

Inverse kinematics (IK) is the use of kinematic equations to determine the joint parameters of a manipulator so that the end effector moves to a desired position; IK can be applied in many areas, including robotics, engineering, computer graphics and video games. In this survey, we present a comprehensive review of the IK problem and the solutions developed over the years from the computer graphics point of view. The paper starts with the definition of forward and IK, their mathematical formulations and explains how to distinguish the unsolvable cases, indicating when a solution is available. The IK literature in this report is divided into four main categories: the analytical, the numerical, the data‐driven and the hybrid methods. A timeline illustrating key methods is presented, explaining how the IK approaches have progressed over the years. The most popular IK methods are discussed with regard to their performance, computational cost and the smoothness of their resulting postures, while we suggest which IK family of solvers is best suited for particular problems. Finally, we indicate the limitations of the current IK methodologies and propose future research directions.

Section: Discussionmentioning

confidence: 99%

Inverse Kinematics Techniques in Computer Graphics: A Survey

Aristidou

Lasenby

Chrysanthou

et al. 2017

193

103

“…We compared our approach with other recent methods; we chose methods that correct motion at the marker level, including matrix factorization [BL16], and methods that work at the joint level, including deep learning [HSKJ15], and sparse coding [FJX * 15]. The experiments were implemented using data containing motion sequences of various locomotion (walk, run, jump), and dancing.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…The biggest advantage of our method compared to other alternatives is its ability to detect the joints that contain errors, and repair only those. In contrast, methods such as Holden et al [HSKJ15] and Feng et al [FJX * 15] repair the global motion at once, smoothing even the rotations of correct joints. This operation has the effect of losing some of the details of the motion, changing the overall posture of the performer, and removing nuance or style (see Figure 14 and the supplemental video, particularly the performers' head and/or arms).…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

Self‐similarity Analysis for Motion Capture Cleaning

Aristidou

Cohen–Or

Hodgins

et al. 2018

Motion capture sequences may contain erroneous data, especially when the motion is complex or performers are interacting closely and occlusions are frequent. Common practice is to have specialists visually detect the abnormalities and fix them manually. In this paper, we present a method to automatically analyze and fix motion capture sequences by using self‐similarity analysis. The premise of this work is that human motion data has a high‐degree of self‐similarity. Therefore, given enough motion data, erroneous motions are distinct when compared to other motions. We utilize motion‐words that consist of short sequences of transformations of groups of joints around a given motion frame. We search for the K‐nearest neighbors (KNN) set of each word using dynamic time warping and use it to detect and fix erroneous motions automatically. We demonstrate the effectiveness of our method in various examples, and evaluate by comparing to alternative methods and to manual cleaning.

“…Data‐driven methods have been proposed to preserve the spatio‐temporal features of highly coordinated human motions [HJ10, FJX*14, XFJ*15]. Recently, deep learning frameworks have shown a high‐quality performance in the correction of motion data [HSKJ15, HSK16, Hol18, MLCC17, LZZ*19]. While the direction that the deep learning framework pursues is promising, its applicability in practice can be limited as it requires training priors, such as noisy or corrupted motion data; the resulting system is inevitably specialized to handle the situations defined by the given data.…”

Section: Related Workmentioning

confidence: 99%

Synthesizing Character Animation with Smoothly Decomposed Motion Layers

Eom

Choi

Cho

et al. 2019

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.