Weekly Gathering for
Vision Researchers and Enthusiasts
11:30 am- 12:30 pm, 32-D507
Title: Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes with Deep Generative Networks
Speaker: Amir Arsalan Soltani
Time: 11:30 am to 12:30 pm
Date: Tuesday, Oct.17, 2017
Agents can take advantage of explicit 3D representations for object manipulation and to handle challenging vision tasks such as learning about the relationships of objects and how to interact with them much more efficiently. Building computational models which are able to obtain 3D representations in an efficient manner and generate 3D shapes with high resolution and details is a first step towards this goal. However, there are important technical challenges to address so that such 3D representations can be effectively deployed as perception systems of artificially intelligent systems. In this talk I will present ourrecently published work on building generative models of generic 3D shapes via multi-view representations. Our work goes beyond the state-of-the-art in keys respects. I will show our results on generating novel shapes randomly and class-conditionally, obtaining 3D reconstructions given a 2D view of an object in real-world settings and analysis of the generated shapes and reconstructions. I will end my talk discussing the role of 3D representations for robotics and object manipulation and will try to establish the connections of using good generative models for 3D shapes and 3D representations in particular in solving inverse problems in vision and planning.
Amir is currently a research assistant at Josh Tenenbaum's lab. He obtained his M.Sc. in Computer Science from University at Buffalo in 2016 and his B.Sc. in Software Engineering from IAUN in Iran in 2012. He is interested in doing research on building computational models for perception to learn inverse models of the environment and enable the AI agents plan for their goals more efficiently.