{"content":[{"text":"Published as a conference paper at ICLR 2018","bbox":{"left":0.176471,"top":0.033035,"width":0.302442,"height":0.015157999999999998}},{"text":"In contrast, our paper provides a robust framework that can synthesize highly complex human motion \n variations of arbitrary styles, such as dancing and martial arts, without querying a database. We \n achieve this by using a novel deep auto-conditioned RNN (acRNN) network architecture. \n Recurrent neural networks are autoregressive deep learning frameworks which seek to predict \n sequences of data similar to a training distribution. Such a framework is intuitive to apply to human \n motion, which can be naturally modeled as a time series of skeletal joint positions. We are not the \n ﬁrst to leverage RNNs for this task (Fragkiadaki et al., 2015; Jain et al., 2016; Bütepage et al., 2017; \n Martinez et al., 2017), and these works produce reasonably realistic output at a number of tasks such \n as sitting, talking, smoking, etc. However, these existing methods also have a critical drawback: the \n motion becomes unrealistic within a couple of seconds and is unable to recover. \n This issue is commonly attributed to error accumulation due to feeding network output back into \n itself (Holden et al., 2017). This is reasonable, as the network during training is given ground-truth \n input sequences to condition its subsequent guess, but at run time, must condition this guess on its \n own output. As the output distribution of the network will not be identical to that of the ground-truth, \n it is in effect encountering a new situation at test-time. The acRNN structure compensates for this \n by linking the network’s own predicted output into its future input streams during training, a similar \n approach to the technique proposed in (Bengio et al., 2015). Our method is light-weight and can \n be used in conjunction with any other RNN based learning scheme. Though straightforward, this \n technique ﬁxes the issue of error accumulation, and allows the network to output incredibly long \n sequences without failure, on the order of hundreds of seconds (see Figure 5). Though we are yet \n as unable to prove the permanent stability of this structure, it seems empirically that motion can be \n generated without end. In summary, we present a new RNN training method capable for the ﬁrst time \n of synthesizing potentially indeﬁnitely long sequences of realistic and complex human motions with \n respect to different styles.","bbox":{"left":0.175966,"top":0.104316,"width":0.6495949999999999,"height":0.348504}},{"text":"2","bbox":{"left":0.176959,"top":0.471881,"width":0.009766999999999998,"height":0.01818900000000001}},{"text":"RELATED WORK","bbox":{"left":0.207238,"top":0.471881,"width":0.137854,"height":0.01818900000000001}},{"text":"Many approaches have developed over the years in order to generate realistic human motion. In this \n section, we ﬁrst review the literature that has dealt with motion synthesis using simulation methods \n and database-driven methods, then review the more recent deep learning approaches.","bbox":{"left":0.176471,"top":0.504118,"width":0.647062,"height":0.04283200000000009}},{"text":"Simulation-based Methods. Simulation-based techniques are able to produce physically plausible \n animations (Geijtenbeek & Pronost, 2012; Levine & Popovi´c, 2012; Clegg et al., 2015; Hämäläinen \n et al., 2015; Ha et al., 2012; Liu et al., 2016), including realistic balancing, motion on terrain of \n various heights, and recovery from falling. Many of these methods consider physical constraints on \n the human skeleton while optimizing an motion objective. For example, in the work of Levine et \n al. (Levine & Popovi´c, 2012), one task they employ is moving the skeleton in a certain direction \n without falling over. Similarly, in Ha et al. (Ha et al., 2012), given initial fall conditions, they seek to \n minimize joint stress due to landing impact while ensuring a desired landing pose. Though the output \n is plausible, they require speciﬁc objectives and constraints for each individual task; it is infeasible to \n apply such explicit priors to highly stylized and varied motion such as dancing or martial arts. There \n are also some recent works (Peng et al., 2017; Merel et al., 2017) which attempt to use less rigid \n objectives, instead employing adversarial and reinforcement learning. However, these motions often \n look uncanny and not human-like.","bbox":{"left":0.175884,"top":0.561278,"width":0.64765,"height":0.1816549999999999}},{"text":"Database-driven Methods. Motion graphs (Kovar et al., 2002; Safonova & Hodgins, 2007; Min & \n Chai, 2012), which stitch transitions into segments queried from a database, can generate locomotion \n along arbitrary paths, but are in essence limited to producing memorized sequences. Frameworks \n based on linear (Safonova et al., 2004; Chai & Hodgins, 2005; Tautges et al., 2011) and kernal \n methods (Mukai, 2011; Park et al., 2002; Levine et al., 2012; Grochow et al., 2004; Moeslund et al., \n 2006; Wang et al., 2008) have also shown reasonable success. Taylor et al. (Taylor & Hinton, 2009) \n use conditional restricted boltzmann machines to model motion styles. Grochow et al. (Grochow \n et al., 2004) and Wang et al. (Wang et al., 2005) both use Gaussian Process Latent Variable Models \n (GPLVM) to control motion. Levine et al. (Levine et al., 2012) apply reinforcement learning in \n a reduced space to compute optimal motions for a variety of motion tasks, including movement, \n punching, and kicking. Kee et al. (Lee et al., 2010) use a representation of motion data called \n motion-ﬁelds which allows users to interactively control movement of a character. The work of Xia","bbox":{"left":0.175933,"top":0.75726,"width":0.6496379999999999,"height":0.1678179999999999}},{"text":"2","bbox":{"left":0.49593,"top":0.947657,"width":0.008139000000000007,"height":0.015158000000000005}}],"images_bbox":[],"images_bbox_not_text_overlap":[]}