IPAB Workshop-04/06/2020
Ian Mason
Title: Modelling Locomotion Styles for Humanoid Animation with Feature-wise Transformations
Abstract: I will be discussing ongoing work on varying the style of locomotion performed by a 3D character using a neural network animation system. We investigate the application of feature-wise transformations in the hidden layers of the network to allow for modifying style in real-time and demonstrate that this method can create reasonable interpolations between seen styles. I will also talk briefly about future directions for this work.
Nanbo Li
Title: Learning Neural Scene Representations of Multi-object Scenes With Multi-view Observations
Abstract: Object-centric scene representations have the potential to support high-level
cognitive abilities like causal reasoning and object-centric exploration. Current
approaches for generative scene representation learning research are either
inaccurate (suffering from single-view spatial ambiguity) or unstructured (failing to attain
object-level scene factorization and interpretation). In this work, we address multi-view and
multi-object representation and inference to resolve spatial ambiguity with spatial exploration.
Through experiments we demonstrate that our method not only learns more accurate and
disentangled object-centric representations, but also does it allow prediction of both
observations and object segmentation for novel viewpoints.
Jun 04 2020
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IPAB Workshop-04/06/2020
Nanbo Li, Ian Mason, Kuba Sanak
https://eu.bbcollab.com/guest/7d9a6997dfb04dce850edf1fa679f608
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Blackboard Collaborate https://eu.bbcollab.com/guest/7d9a6997dfb04dce850edf1fa679f608