10th Workshop on Human Behavior Understanding (HBU) Call for Papers

10th Workshop on Human Behavior Understanding (HBU)
In conjunction with International Conference on Computer Vision (ICCV) 2019
27 October 2019, Seoul, Korea

Focus Theme: Generating, Forging and Detecting Fake Human Behavioral Data



As in many other computer vision tasks, deep learning has brought
revolutionary advances in human behaviour understanding from visual
data. Deep models are now extremely effective not only in detecting
and analyzing human faces, bodies and collective activities but also
in generating realistic human-like behavioral data. From full-body
deepfakes to AI-based translation dubbing, deep networks can now
synthesize images and videos of humans such as they are virtually
indistinguishable from real ones. The workshop will focus on recent
advances and novel methodologies for generating human behaviour data,
with special emphasis on approaches for forging images and videos
depicting real-looking human faces and/or full bodies and on
algorithms for detecting fake human-like visual data.

The HBU workshops, organized since 2010 as satellite to ICPR'10,
AMI'11, IROS'12, ACM Multimedia'13, ECCV'14 and
UBICOMP'15, ACM Multimedia'16, FG'18, ECCV'18 Conferences,
aim to inspect developments in areas where smarter computers that can
sense human behavior. These events have a unique aspect of fostering
cross-pollination of different disciplines, bringing together
researchers of mobile and ubiquitous computing, computer vision,
multimedia, robotics, HCI, artificial intelligence, pattern
recognition, interaction design, ambient intelligence, and
psychology. The diversity of human behavior, the richness of
multi-modal data that arises from its analysis, and the multitude of
applications that demand rapid progress in this area ensure that the
HBU Workshops provide a timely and relevant discussion and
dissemination platform.

Each edition of the HBU workshop had a different focus theme, dealing
with a newly emerging topic or question in the automatic analysis of
human behavior. The focus theme of this year is of high interest for
computer vision researchers: Generating, Forging and Detecting Fake
Human Behavioral Data. The automatic generation of visual contents is
currently a very hot topic in the community. With this edition of the
HBU workshops, we attempt to foster research on how to generate visual
data (still images and videos) describing human behavior both from the
applicative and methodological points of view.




ICCV'2019 HBU workshop, in addition to covering the main themes of
human behavior understanding, deals with generating human behavior
data, with special emphasis on methodologies and approaches for
forging images and videos depicting real-looking human faces and/or
full bodies and on algorithms for detecting fake human-like visual
data. Contributions based on deep neural architectures are welcome, as
well as methods based on other techniques (e.g. parametric
models). These contributions could address the following topics:

Human Behavior Analysis Systems

    Action and activity recognition
    Affect analysis
    Face analysis
    Gaze, attention and saliency
    Gestures and haptic interaction
    Social signal processing
    Voice and speech analysis
    Theoretical frameworks of behavior analysis
    Data collection, annotation, and benchmarking
    User studies and human factors

Generating Visual data of Human Behavior

    Methods for face synthesis and modification of facial attributes (e.g. age, expression).
    Approaches for generating human bodies and altering their properties (e.g. 3D pose, clothes).
    Techniques for forging human-like behavioral data
    Methodologies for counteracting adversarial attacks.
    Techniques for synthesizing visual data depicting collective human behaviour.
    Novel deep generative models for sequence-like data generation.
    Approaches to synthesize multi-modal human behavioral data.
    Applications (e.g. surveillance, entertainment, autonomous driving, fashion, robotics).

Papers must be submitted online through the CMT submission system at:
and will be double-blind peer reviewed by at least two reviewers.
Submissions should conform to the ICCV 2019 proceedings style.

We expect two kind of submissions:

    Full papers of new contributions (8 pages NOT including references)
    Short papers describing incremental/preliminary work (2 pages NOT including references)

More info at: https://project.inria.fr/whbu/

Regular Paper Submission: July 1st, 2019
Extended Abstract Submission: July 15th, 2019
Notification of Acceptance: July 31st, 2019
Camera-Ready: August 15th, 2019


Cristian Sminchisescu, Google & Lund University, DE
Hao Li, University of Southern California, USA

Xavier Alameda-Pineda, Inria, FR.
Xiaoming Liu, Michigan State University, USA.
Elisa Ricci, FBK & University of Trento, IT.
Albert Ali Salah, Bogaziši University, TR & Utrecht University, NL.
Nicu Sebe, University of Trento, IT.
Sergey Tulyakov, Snap Research, USA.