CALL FOR PAPERS

Human behavior, both for face (e.g., expressions) and body has been studied in detail mainly (for example for expression / action classification and prediction), but there have been few works exploring generation of novel behaviors. Generating novel sequences of human facial expression, talking heads, or body to form a natural and plausible action with continuous and smooth temporal dynamics is a challenging problem. These motions can either simulate full body movement, like for gait, or part specific movement, like in playing the guitar or phone call, or involve facial expressions, action units or even mouth movement when a person speaks or reads a text. With the advent of powerful generative models such as Generative Adversarial Networks (GAN), or Diffusion models, novel data generation paradigms have become possible, and these networks have shown to be powerful in many image generation tasks. However, many issues remain to be solved especially when passing from the static to the dynamic case and new research problems emerge. For example, what are the appropriate architectures and loss functions to generate dynamic facial expressions, actions, in 2D or in 3D? Which are the best objective and subjective methods to evaluate the generative models? Is it possible to generate long dynamic sequences with natural transitions between different expressions or actions?

We expect generating synthetic and realistic static and dynamic data of humans can have a big impact in several different contexts. A straightforward outcome that developing such techniques could have, is that of generating an abundance and variety of new data that could be otherwise difficult, very expensive and time consuming to obtain from reality. Such data can be essential in simulation, virtual and augmented reality, in training more robust learning tools, to cite a few. For example, we could expect new applications in the game and movie industry, where fully synthetic actors could be used in the near future, without the need of explicit modeling. We expect this workshop could help to make a step in these research directions, also focusing on new evaluation methodologies that could make quantitative rather than qualitative the assessment and comparison of generated data. With respect to this latter aspect, we expect the public release of new benchmarks especially in 3D could help to improve the research in this field. Finally, there is quite debate in society and in the scientific community too about ethical implications related to generating synthetic and realistic humans’ data. We also aim to have discussion on these social and ethical implications at the workshop. The goal of this workshop is to provide contributions to this emerging field of study.

TOPICS

The goal of this workshop is to provide contributions to this emerging field of study. Topics of interest of this workshop include but are not limited to:

• 2D and 3D static and dynamic content generation

• 2D and 3D facial expression generation

• Talking heads generation

• Actions generation

• Behavior generation

• Evaluation of generative models

• New benchmarks

• Reduce ethical issues in human data generation

• Applications (data augmentation, simulation, VA, medical, robotics)

The workshop will take place on September 11st 2023. The GHB workshop is organized in conjunction with ICIAP 2023  that will be held in Udine, Italy on 11-15th September, 2023.