5 papers
Imagining the Unseen: Generative Location Modeling for Object Placement
Jooyeol Yun, Davide Abati, Mohamed Omran +3
Location modeling, or determining where non-existing objects could feasibly appear in a scene, has the potential to benefit numerous computer vision tasks, from automatic object in…
The 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real): Methods and Results
Qiuyu Chen, Xin Jin, Yue Song +45
This paper reviews the 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real), held in conjunction with ICCV 2025. The workshop a…
Controllable 3D Placement of Objects with Scene-Aware Diffusion Models
Mohamed Omran, Dimitris Kalatzis, Jens Petersen +2
Image editing approaches have become more powerful and flexible with the advent of powerful text-conditioned generative models. However, placing objects in an environment with a pr…
Scene-Aware Location Modeling for Data Augmentation in Automotive Object Detection
Jens Petersen, Davide Abati, Amirhossein Habibian +1
Generative image models are increasingly being used for training data augmentation in vision tasks. In the context of automotive object detection, methods usually focus on producin…
Gaussian Splatting is an Effective Data Generator for 3D Object Detection
Farhad G. Zanjani, Davide Abati, Auke Wiggers +4
We investigate data augmentation for 3D object detection in autonomous driving. We utilize recent advancements in 3D reconstruction based on Gaussian Splatting for 3D object placem…