10 research outputs found
Capture, Learning, and Synthesis of 3D Speaking Styles
Audio-driven 3D facial animation has been widely explored, but achieving
realistic, human-like performance is still unsolved. This is due to the lack of
available 3D datasets, models, and standard evaluation metrics. To address
this, we introduce a unique 4D face dataset with about 29 minutes of 4D scans
captured at 60 fps and synchronized audio from 12 speakers. We then train a
neural network on our dataset that factors identity from facial motion. The
learned model, VOCA (Voice Operated Character Animation) takes any speech
signal as input - even speech in languages other than English - and
realistically animates a wide range of adult faces. Conditioning on subject
labels during training allows the model to learn a variety of realistic
speaking styles. VOCA also provides animator controls to alter speaking style,
identity-dependent facial shape, and pose (i.e. head, jaw, and eyeball
rotations) during animation. To our knowledge, VOCA is the only realistic 3D
facial animation model that is readily applicable to unseen subjects without
retargeting. This makes VOCA suitable for tasks like in-game video, virtual
reality avatars, or any scenario in which the speaker, speech, or language is
not known in advance. We make the dataset and model available for research
purposes at http://voca.is.tue.mpg.de.Comment: To appear in CVPR 201
Bridging RL Theory and Practice with the Effective Horizon
Deep reinforcement learning (RL) works impressively in some environments and
fails catastrophically in others. Ideally, RL theory should be able to provide
an understanding of why this is, i.e. bounds predictive of practical
performance. Unfortunately, current theory does not quite have this ability. We
compare standard deep RL algorithms to prior sample complexity prior bounds by
introducing a new dataset, BRIDGE. It consists of 155 MDPs from common deep RL
benchmarks, along with their corresponding tabular representations, which
enables us to exactly compute instance-dependent bounds. We find that prior
bounds do not correlate well with when deep RL succeeds vs. fails, but discover
a surprising property that does. When actions with the highest Q-values under
the random policy also have the highest Q-values under the optimal policy, deep
RL tends to succeed; when they don't, deep RL tends to fail. We generalize this
property into a new complexity measure of an MDP that we call the effective
horizon, which roughly corresponds to how many steps of lookahead search are
needed in order to identify the next optimal action when leaf nodes are
evaluated with random rollouts. Using BRIDGE, we show that the effective
horizon-based bounds are more closely reflective of the empirical performance
of PPO and DQN than prior sample complexity bounds across four metrics. We also
show that, unlike existing bounds, the effective horizon can predict the
effects of using reward shaping or a pre-trained exploration policy
Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping
Inverse reinforcement learning (IRL) is computationally challenging, with
common approaches requiring the solution of multiple reinforcement learning
(RL) sub-problems. This work motivates the use of potential-based reward
shaping to reduce the computational burden of each RL sub-problem. This work
serves as a proof-of-concept and we hope will inspire future developments
towards computationally efficient IRL
Knocking at the gate: The path to publication for entrepreneurship experiments through the lens of gatekeeping theory
We draw on gatekeeping theory to explore the individual and routine-level criticisms that entrepreneurship experimentalists receive during the review process. Using a multi-study approach, we categorize common gatekeeping themes and present illustrative critiques derived from a unique sample of decision letters and a supplemental survey of entrepreneurship editors. In combination, we extend gatekeeping theory by considering how it applies to the scholarly domain, contribute to the literature by exploring an alternative theoretical explanation as to why entrepreneurship experiments might fail to survive the review process, and finally, provide contextualized recommendations for authors and reviewers of experimental research