The 6th GENEA Workshop @ACM Multimedia 2025 – Generation and Evaluation of Non-verbal Behaviour for Embodied Agents
March 26th, 2025 
Daniela Lopez de Luise The 6th Generation and Evaluation of Non-verbal Behaviour for Embodied Agents (GENEA) Workshop
October 27 or 28, 2025 (in person)
Held in conjunction with ACM Multimedia 2025, Dublin, Ireland
Website: https://genea-workshop.github.io/2025/workshop/
We are pleased to announce the Call for Papers for the 6th edition of the GENEA Workshop, focusing on the generation of non-verbal behaviours such as gesticulation, facial expressions, and gaze—a crucial component of natural interaction with embodied agents, including virtual agents and social robots.
Currently, behaviour generation is typically powered by rule-based systems, data-driven approaches like generative AI, or hybrid models. For evaluation, both objective and subjective methods are used, though their application and validity are often debated. This workshop aims to bring together researchers from diverse disciplines working on various aspects of non-verbal behaviour generation, facilitating discussion on advancing both generation techniques and evaluation methodologies.
✨ Topics of Interest
We invite original contributions on topics including (but not limited to):
- Automated synthesis of facial expressions, gestures, and gaze movements, including multimodal synthesis
- Audio-, music-, emotion-driven, or stylistic non-verbal behaviour synthesis
- Closed-loop / end-to-end non-verbal behaviour generation (from perception to action)
- Non-verbal behaviour synthesis in two-party and group interactions
- Use of LLMs/VLMs in the context of non-verbal behaviour synthesis
- New datasets, annotation methods, and analyses of existing datasets related to non-verbal behaviour
- Cross-cultural and multilingual influences on non-verbal behaviour generation
- Cognitive and affective models for non-verbal behaviour generation
- Social perception and attribution of synthesised non-verbal behaviour
- Ethical considerations and biases in non-verbal behaviour synthesis
- Subjective and objective evaluation methods for any of the above topics
📝 Submission Types
We welcome:
- Long papers (8 pages)
- Short papers (4 pages)
All submissions should follow the double-column ACM conference format used by ACM Multimedia (https://acmmm2025.org/call-for-papers/). Pages containing only references do not count toward the page limit. Papers must be submitted in PDF format via OpenReview and formatted for double-blind review. Accepted papers will be presented at the workshop and included in the companion proceedings.
🗓️ Important Dates (Anywhere on Earth, AoE)
- Paper abstract deadline: 2 July 2025
- Full submission deadline: 4 July 2025
- Notification of acceptance: 21 July 2025
- Camera-ready deadline: 30 July 2025
- Poster submission deadline: 19 September 2025
- Notification of poster acceptance: 3 October 2025
- Workshop date: 27 or 28 October 2025
👥 Organisers
- Taras Kucherenko – Electronic Arts (EA), Sweden
- Rajmund Nagy – KTH Royal Institute of Technology, Sweden
- Alice Delbosc – Davi, The Humanizers, France
- Oya Celiktutan – King's College London, United Kingdom
- Youngwoo Yoon – ETRI, South Korea
- Gustav Eje Henter – KTH Royal Institute of Technology / Motorica AB, Sweden
- Laura Hensel – University of Glasgow, Scotland, United Kingdom
For more information, visit our website, contact us at geneaworkshop2020@gmail.com, or follow us:
🔵 @geneaworkshop.bsky.social (BlueSky)
🐦 @genea_workshop (X)
💼 LinkedIn Group
We look forward to your contributions!
Call for Participation & Papers – ACAIN 2025 – 5th International Advanced Course & Symposium on Artificial Intelligence & Neuroscience, September 21-24 2025, Riva del Sole Resort & SPA, Tuscany * Course Registration: April 23 * Paper Submission: May 23 *
March 26th, 2025 Daniela Lopez de Luise 
The 5th International Advanced *Course* & *Symposium* on Artificial Intelligence & Neuroscience, September 21-24, 2025, Riva del Sole Resort & SPA, Castiglione della Pescaia (Grosseto) Tuscany, Italy
W: https://acain2025.icas.events
E: acain@icas.cc
Symposium Deadline:
Paper Submission (Symposium): by May 23 (AoE)
https://acain2025.icas.events/symposium-call-for-papers/
https://easychair.org/conferences/?conf=acain2025
Course Deadline:
Regular Registration (Course): by April 23 (AoE)
https://acain2025.icas.events/course-description/
https://acain2025.icas.events/registration/
https://2025.iaiss.cc/deadlines/
LECTURERS:
Panos Pardalos, University of Florida, USA
Jose Principe, University of Florida, USA
Maneesh Sahani, University College London, UK
Jonathon Shlens, Google DeepMind, USA
Dimitra Thomaidou, Hellenic Pasteur Institute, Greece
Marina Vidaki, University of Crete, Medical School, Crete, Greece
More Speakers TBA
https://acain2025.icas.events/course-lecturers/
SCOPE & MOTIVATION:
ACAIN: AI meets Computational Neuroscience and Cognitive Science
The ACAIN symposium and course is an interdisciplinary event featuring leading scientists from AI and Neuroscience, providing a special opportunity to learn about cutting-edge research in the fields of AI, Neuroscience, Neuroscience-Inspired AI, Human-Level AI, and Cognitive Science.
The Advanced Course and Symposium on Artificial Intelligence & Neuroscience (ACAIN) is a full-immersion four-day Course and Symposium in Tuscany on cutting-edge advances in Artificial Intelligence and Neuroscience with lectures delivered by world-renowned experts. The Course provides a stimulating environment for academics, early career researchers, Post-Docs, PhD students and industry leaders. Participants will also have the chance to present their results with oral talks or posters, and to interact with their colleagues, in a convivial and productive environment.
Two days of keynote talks and oral presentations, the ACAIN Symposium, (September 23-24), will be preceded by lectures of leading scientists, the ACAIN Course, (September 21-22).
Bringing together AI and neuroscience promises to yield benefits for both fields. The future impact and progress in both AI and Neuroscience will strongly depend on continuous synergy, exchange, collaboration and efficient cooperation between the two research communities. These are the goals of the International Course and Symposium – ACAIN, which is aimed both at AI experts with interests in Neuroscience and at neuroscientists with an interest in AI. ACAIN accepts rigorous research that promotes and fosters multidisciplinary interactions between artificial intelligence and neuroscience.
The Advanced Course is suited for scholars, academics, early career researchers, Post-Docs, PhD students and industry leaders.
The Event (Course and Symposium) will involve a total of 36-40 hours of lectures. Academically, this will be equivalent to 8 ECTS points for the PhD Students and the Master Students attending the Event.
SYMPOSIUM PROGRAM COMMITTEE (partial list, confirmed members):
https://acain2025.icas.events/program-committee/
ORGANIZING COMMITTEE:
https://acain2025.icas.events/symposium-committee/
VENUE & ACCOMMODATION:
https://acain2025.icas.events/venue/
ACAIN is a *Residential Conference*, all participants (invited speakers, authors, organizers, chairs, participants) must book and stay at the Riva del Sole Resort and Spa. No exceptions are allowed.
Riva del Sole Resort & SPA
a: Località Riva del Sole – Castiglione della Pescaia (Grosseto) CAP 58043, Tuscany – Italy
p: +39-0564-928111
f: +39-0564-935607
e: booking.events@rivadelsole.it
w: www.rivadelsole.it/en
ACTIVITIES:
https://acain2025.icas.events/activities/
REGISTRATION:
https://acain2025.icas.events/registration/
See you in Tuscany in September!
ACAIN Organizing Committee.
E: acain@icas.cc
W: https://acain2025.icas.events
FB: https://www.facebook.com/ACAIN.LakeDistrict
Obviously, this is only a Call for Participation & Call for Papers, to have complete and updated information we recommend you access the relevant website: https://acain2025.icas.events
CFP ICIP 2025 Satellite Workshop: “Bridging the Gap: Advanced Data Processing for Natural Disaster Management Integrating Visual and Non-Visual Insights”
March 26th, 2025 Daniela Lopez de Luise Natural disasters are becoming more frequent and more intense. Our ability to respond effectively to such events depends strongly, among other things, on timely and accurate processing of diverse sources of data, ranging from satellite imagery and camera-equipped drones to in-situ sensor networks and weather models. Imagery data from satellite and airborne platforms, alongside AI-based processing methods, is crucial in disaster management. However, a significant gap remains between visual data (like images and videos) and non-visual data (such as meteorological data, smoke models, and social media reports), and bridging this gap is key to enhancing disaster response and decision-making. The „Advanced Data Processing for Natural Disaster Management – Integrating Visual and Non-Visual Insights“ is designed to specifically address these challenges.
The workshop is planned as a satellite workshop of the 2025 IEEE International Conference on Image Processing (ICIP) that will take place in Anchorage, Alaska, USA. IEEE ICIP 2025 – a flagship conference of the IEEE Signal Processing Society – explores the latest technical innovations in image and video processing and covers cutting-edge topics involving computer vision.
We welcome potential authors to submit their contributions in the following areas:
- Joint image/video processing and computational fluid dynamics for local weather modeling
- Modeling smoke propagation around fires constrained with visual information clues
- Fire nest detection through visual and robotic olfaction methods for smoke source localization
- Numerical flood simulation models constrained by satellite or drone imagery
- Joint analysis of social media posts and visual/in-situ information
While the focus is on forest fires and floods, other extreme events, e.g., volcanos are also relevant.
This is an excellent chance to contribute groundbreaking research on integrating visual and non-visual insights to enhance disaster response strategies. Share your expertise and be part of the conversation shaping the future of disaster management!
📅 Submission Deadline: 28 May 2025
🔗 Workshop website: ICIP 2025 Satelite Workshop Bridging the Gap: Advanced Data Processing for Natural Disaster Management Integrating Visual and Non-Visual Insights – Icarus | CVML Research
Looking forward to your contribution!
The workshop organizers,
Dmitriy Shutin, German Aerospace Center (DLR), Munich, Germany
Vasileios Mygdalis, Aristotle University of Thessaloniki, Greece
IEEE TBIOM Special Issue on Generative AI and Large Vision-Language Models for Biometrics
March 26th, 2025 Daniela Lopez de Luise Special Issue on
Generative AI and Large Vision-Language Models for Biometrics
Submission Deadline: 31 May 2025
Targeted Publication: Q1 2026
Paper submission: https://ieee.atyponrex.com/journal/tbiom
*********************************************************************************************
*** Motivation ***
In the rapidly advancing field of artificial intelligence, generative AI
and large-scale vision-language models are becoming key areas of
interest, revolutionizing numerous research fields, including natural
language processing and computer vision. Generative AI models are
designed and trained to approximate the underlying distribution of a
dataset, enabling the generation of new samples that reflect the
patterns and regularities within the training data. Among the various
types of generative models, such as Generative Adversarial Networks
(GANs), Variational Autoencoders (VAEs), flow-based, autoregressive, and
diffusion models, GANs and diffusion models have gained significant
attention and are widely applied to tasks such as image synthesis, image
manipulation, text generation, and speech synthesis. These models have
shown remarkable success in modeling and interpreting the probability
distributions of real-world data. Vision-language models, on the other
hand, integrate visual and textual data, learning to associate these
modalities to enhance understanding and enable multimodal
reasoning-based applications.
The advancements in generative AI and vision-language models (LVMs) are
also making a significant impact on biometrics, offering new
possibilities for addressing longstanding challenges. Generative AI,
with its ability to synthesize highly realistic data, has the potential
to address privacy concerns related to collecting, sharing, and using
sensitive biometric data. This synthetic data can also be used to
increase diversity and variation in training datasets through
augmentation, thus improving model generalizability and reducing
potential bias induced by imbalanced training data. At the same time,
large vision-language models offer the capability to process and
understand multimodal information by combining visual features with
contextual data, such as semantic insights from natural language.
Furthermore, large-scale vision-language models can be optimized for
downstream tasks, such as template extraction, using zero or few-shot
learning approaches, making them highly versatile for biometric
applications.
Although generative AI and vision-language models offer a rich set of
tools that can be utilized to address challenges in biometrics, the
misuse of these technologies presents a threat to the field. Generative
AI models have the ability to incorporate conditions in the generation
process to take control over the generated samples. This enables a wide
range of applications such as image-to-image translation, text-to-image
synthesis, and style transfer. However, this capability also allows for
creating deepfake attacks, e.g., images, videos, and audio that are
indistinguishable or nearly indistinguishable from real content. The
increased realism and widespread public accessibility of generative AI
have raised concerns about the potential misuse of this technology for
malicious purposes. This highlights the need for solutions to detect
generated AI content and mitigate the potential misuse of generative AI
models.
The proposed TBIOM special issue will provide a platform to discuss the
latest advancements and technical achievements related to Generative AI
and Large vision-language models when applied to problems in biometrics.
The topics of interest of the special issue include, but are not limited to:
+ Novel generative AI models for responsible synthesis of biometric data
+ Novel generative models for conditional data synthesis
+ Biometrics interpretability and explainability through large
language-vision models
+ Few-shot learning from large language-vision models
+ Generative AI and LVMs for detecting attacks on biometrics systems
+ Generative AI-based image restoration
+ Information leakage of synthetic data
+ Data factories and label generation for biometric models
+ Quality assessment of AI generated data
+ Synthetic data for data augmentation
+ Detection of generated AI contents
+ Bias mitigation using synthetic data
+ LLMs and VLMs for biometrics
+ Watermarking AI generated content
+ New synthetic datasets and performance benchmarks
+ Security and privacy issues regarding the use of generative AI methods
for biometrics
+ Ethical considerations regarding the use of generative AI methods for
biometrics
+ Parameter efficient fine-tuning of VLMs for biometrics applications
*** Important Dates ***
Submission deadline: 31 May 2025
First round of reviews completed (first decision): August 2025
Second round of reviews completed October 2025
Final papers due December 2025
Publication date: Q1 2026
*** Paper Submission ***
Papers should be submitted through the TBIOM submission portal before
the deadline using the TBIOM journal templates:
https://ieee.atyponrex.com/journal/tbiom and selecting the article type:
“Generative AI and Large Vision-Language Models for Biometrics”.
*** Guest Editors: ***
+ Fadi Boutros, Fraunhofer IGD, Germany
+ Hu Han, Institute of Computing Technology, Chinese Academy of Sciences
(CAS), China
+ Tempestt Neal, University of South Florida, United States
+ Vishal M. Patel, Johns Hopkins University, United States
+ Vitomir Štruc, University of Ljubljana, Slovenia
+ Yunhong Wang, Beihang University, China