Gamasutra

Plants & Humans

Thursday, 28 July, 10:45 am - 12:15 pm, Anaheim Convention Center, Ballroom E
Session Chair: Richard (Hao) Zhang, Simon Fraser University

Modeling Dense Inflorescences

Showy inflorescences are a common feature of many plants. This paper presents a suite of biologically motivated algorithms for modeling inflorescences with closely packed flowers. The method accounts for neighbor-dependent floret types, petal collisions, and branching structures specific to inflorescences.

Andrew Owens
University of Calgary

Mikolaj Cieslak
University of Calgary

Jeremy Hart
University of Calgary

Regine Classen-Bockhoff
Johannes Gutenberg-Universität Mainz

Przemyslaw Prusinkiewicz
University of Calgary

Spectral Style Transfer for Human Motion Between Independent Actions

Introducing a spectral style-transfer method for human motion between independent actions, thereby greatly reducing the required effort and cost of creating all-inclusive motion capture-style databases. The method leverages the spectral domain representation of the human motion to formulate a spatial-correspondence independent approach.

Mehmet Ersin Yumer
Adobe Research

Niloy Mitra
University College London

A Deep Learning Framework for Character Motion Synthesis and Editing

This deep learning framework for synthesizing character movements based on the user's high-level instructions uses a prior in the form of a convolutional auto-encoder trained on a large motion capture dataset.

Daniel Holden
University of Edinburgh

Jun Saito
Marza Animation Planet Inc.

Taku Komura
University of Edinburgh

PiGraphs: Learning Interaction Snapshots From Observations

A probabilistic model connecting human poses and arrangements of objects from observations of interactions collected with commodity RGB-D sensors. This model is encoded as a set of Prototypical Interaction Graphs (PiGraphs): a human-centric representation capturing physical contact and attention linkages between geometry and the human body.

Manolis Savva
Stanford University

Angel Chang
Stanford University

Pat Hanrahan
Stanford University

Matthew Fisher
Stanford University

Matthias Nießner
Stanford University