[Colloquium] Re: REMINDER: 10/19 Young Researcher Seminar Series: Angel Chang, Stanford

[Mary Marre via Colloquium]

*When:*     Wednesday, October 19th at 11:00am

*Where: *   TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526

*Who:*       Angel Chang, Stanford


*Title:*       Learning Spatial Priors for Text to 3D Scene Generation

*Abstract*: The ability to form a visual interpretation of the world from
natural language is pivotal to human communication.  Being able to map
descriptions of scenes to 3D geometric representations can be useful in
many applications such as robotics and conversational assistants.  In this
talk, I will present the task of text to 3D scene generation, where a scene
description in natural language is automatically converted into a plausible
3D scene interpretation.  For example, the sentence "a living room with a
red couch and TV" should generate a realistic living room arrangement with
the TV in front of the couch and supported by a TV stand.  This task lies
at the intersection of NLP and computer graphics, and requires techniques
from both.

A key challenge in this task is that the space of geometric interpretations
is large while natural language text is typically under-specified, omitting
shared, common-sense facts about the world.  I will describe how we can
learn a set of spatial priors from virtual environments, and use them to
infer plausible arrangements of objects given a natural language
description.  I will show that a parallel corpus of virtual 3D scenes and
natural language descriptions can be leveraged to extract likely couplings
between references and concrete 3D objects (e.g., an "L-shaped red couch",
and the virtual geometric representation of that object).  Finally, I will
discuss a few exciting directions for future work at the intersection of
NLP, graphics, and more broadly AI.

*Bio*: Angel Chang recently received her PhD after working in the Stanford
NLP group where she was advised by Chris Manning.  Her research focuses on
the intersection of natural language understanding, computer graphics, and
AI.  She is currently a postdoctoral researcher with Tom Funkhouser at the
Princeton graphics and vision group.  More details at
http://angelxuanchang.github.io

Host: Kevin Gimpel, kgimpel at ttic.edu


*************************



The TTIC Young Researcher Seminar Series (http://www.ttic.edu/young-
researcher.php) features talks by Ph.D. students and postdocs whose research is
of broad interest to the computer science community. The series provides an
opportunity for early-career researchers to present recent work to and meet
with students and faculty at TTIC and nearby universities.


The seminars are typically held on Wednesdays at 11:00am in TTIC Room 526.

For additional information, please contact Matthew Walter (mwalter at ttic.edu
).




Mary C. Marre
Administrative Assistant
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 504*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*

On Tue, Oct 18, 2016 at 12:15 PM, Mary Marre <mmarre at ttic.edu> wrote:

> *When:*     Wednesday, October 19th at 11:00am
>
> *Where: *   TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526
>
> *Who:*       Angel Chang, Stanford
>
>
> *Title:*       Learning Spatial Priors for Text to 3D Scene Generation
>
> *Abstract*: The ability to form a visual interpretation of the world from
> natural language is pivotal to human communication.  Being able to map
> descriptions of scenes to 3D geometric representations can be useful in
> many applications such as robotics and conversational assistants.  In this
> talk, I will present the task of text to 3D scene generation, where a scene
> description in natural language is automatically converted into a plausible
> 3D scene interpretation.  For example, the sentence "a living room with a
> red couch and TV" should generate a realistic living room arrangement with
> the TV in front of the couch and supported by a TV stand.  This task lies
> at the intersection of NLP and computer graphics, and requires techniques
> from both.
>
> A key challenge in this task is that the space of geometric
> interpretations is large while natural language text is typically
> under-specified, omitting shared, common-sense facts about the world.  I
> will describe how we can learn a set of spatial priors from virtual
> environments, and use them to infer plausible arrangements of objects given
> a natural language description.  I will show that a parallel corpus of
> virtual 3D scenes and natural language descriptions can be leveraged to
> extract likely couplings between references and concrete 3D objects (e.g.,
> an "L-shaped red couch", and the virtual geometric representation of that
> object).  Finally, I will discuss a few exciting directions for future work
> at the intersection of NLP, graphics, and more broadly AI.
>
> *Bio*: Angel Chang recently received her PhD after working in the
> Stanford NLP group where she was advised by Chris Manning.  Her research
> focuses on the intersection of natural language understanding, computer
> graphics, and AI.  She is currently a postdoctoral researcher with Tom
> Funkhouser at the Princeton graphics and vision group.  More details at
> http://angelxuanchang.github.io
>
> Host: Kevin Gimpel, kgimpel at ttic.edu
>
>
> *************************
>
>
>
> The TTIC Young Researcher Seminar Series (http://www.ttic.edu/young-
> researcher.php) features talks by Ph.D. students and postdocs whose
> research is of broad interest to the computer science community. The
> series provides an opportunity for early-career researchers to present
> recent work to and meet with students and faculty at TTIC and nearby
> universities.
>
>
> The seminars are typically held on Wednesdays at 11:00am in TTIC Room 526.
>
> For additional information, please contact Matthew Walter (
> mwalter at ttic.edu).
>
>
> Mary C. Marre
> Administrative Assistant
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Room 504*
> *Chicago, IL  60637*
> *p:(773) 834-1757 <%28773%29%20834-1757>*
> *f: (773) 357-6970 <%28773%29%20357-6970>*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
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