AUTO-CONDITIONED RECURRENT NETWORKS FOR EXTENDED COMPLEX HUMAN MOTION SYNTHESIS
We present a real-time method for synthesizing highly complex human motions using a novel training regime we call the auto-conditioned Recurrent Neural Network (acRNN). Recently, researchers have attempted to synthesize new motion by using autoregressive techniques, but existing methods tend to freeze or diverge after a couple of seconds due to an accumulation of errors that are fed back into the network. Furthermore, such methods have only been shown to be reliable for relatively simple human motions, such as walking or running. In contrast, our approach can synthesize arbitrary motions with highly complex styles, including dances or martial arts in addition to locomotion. The acRNN is able to accomplish this by explicitly accommodating for autoregressive noise accumulation during training. Our work is the first to our knowledge that demonstrates the ability to generate over 18,000 continuous frames (300 seconds) of new complex human motion w.r.t. different styles.
The problem of learning auto-regressive (data-driven) human motion models that have long-term stability
is of ongoing interest. Steady progress is being made on this problem, and this paper adds to that.
The paper is clearly written. The specific form of training (a fixed number of self-conditioned predictions,
followed by a fixed number of ground-truth conditioned steps) is interesting for simplicity and its efficacy.
The biggest open question for me is how it would compare to the equally simple stochastic version proposed
by the scheduled sampling approach of [Bengio et al. 2015].
PROS:  The paper provides a simple solution to a problem of interest to many.
CONS:  It is not clear if it improves over something like scheduled sampling, which is a stochastic predecessor
       of the main idea introduced here. The "duration of stability" is a less interesting goal than
       actually matching the distribution of the input data.
The need to pay attention to the distribution-mismatch problem for sequence prediction problems
has been known for a while. In particular, the DAGGER (see below) and scheduled sampling algorithms (already cited) 
target this issue, in addition to the addition of progressively increasing amounts of noise during training
(Fragkiadaki et al). Also see papers below on Professor Forcing, as well as "Learning Human Motion Models
for Long-term Predictions" (concurrent work?), which uses annealing over dropout rates to achieve stable long-term predictions.
  DAGGER algorithm (2011):  http://www.jmlr.org/proceedings/papers/v15/ross11a/ross11a.pdf
  "A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning"
  Professor Forcing (NIPS 2016)
  http://papers.nips.cc/paper/6099-professor-forcing-a-new-algorithm-for-training-recurrent-networks.pdf
  Learning Human Motion Models for Long-term Predictions (2017)
  https://arxiv.org/abs/1704.02827
  https://www.youtube.com/watch?v=PgJ2kZR9V5w
  
While the motions do not freeze, do the synthesized motion distributions match the actual data distributions?
This is not clear, and would be relatively simple to evaluate.  Is the motion generation fully deterministic?
It would be useful to have probabilistic transition distributions that match those seen in the data.
An interesting open issue (in motion, but also of course NLP domains) is that of how to best evaulate
sequence-prediction models.  The duration of "stable prediction" does not directly capture the motion quality. 
Figure 1:  Suggest to make u != v for the purposes of clarity, so that they can be more easily distinguished.
Data representation:
Why not factor out the facing angle, i.e., rotation about the vertical axis, as done by Holden et al, and in a variety of
previous work in general?
The representation is already made translation invariant. Relatedly, in the Training section,
data augmentation includes translating the sequence: "rotate and translate the sequence randomly".
Why bother with the translation if the representation itself is already translation invariant?
The video illustrates motions with and without "foot alignment".
However, no motivation or description of "foot alignment" is given in the paper.
The following comment need not be given much weight in terms of evaluation of the paper, given that the
current paper does not use simulation-based methods. However, it is included for completeness.
The survey of simulation-based methods for modeling human motions is not representative of the body of work in this area
over the past 25 years.  It may be more useful to reference a survey, such as 
"Interactive Character Animation Using Simulated Physics: A State‐of‐the‐Art Review" (2012)
An example of recent SOTA work for modeling dynamic motions from motion capture, including many
highly dynamic motions, is "Guided Learning of Control Graphs for Physics-Based Characters" (2016)
More recent work includes "Learning human behaviors from motion capture by adversarial imitation", 
"Robust Imitation of Diverse Behaviors", and "Deeploco: Dynamic locomotion skills using hierarchical deep reinforcement learning", all of which demonstrate imitation of various motion styles to various degrees.
It is worthwhile acknowledging that the synthesized motions are still low quality, particular when rendered with more human-like looking models, and readily distinguishable from the original motions.  In this sense, they are not comparable to the quality of results demonstrated in recent works by Holden et al. or some other recent works.  However, the authors should be given credit for including some results with fully rendered characters, which much more readily exposes motion flaws.
The followup work on [Lee et al 2010 "Motion Fields"] is quite relevant:
"Continuous character control with low-dimensional embeddings"
In terms of usefulness, being able to provide some control over the motion output is a more interesting problem than
being able to generate long uncontrolled sequences.  A caveat is that the methods are not applied to large datasets.