Georgios Paschalidis — accepted AI-conference papers

2026

RHINO: Reconstructing Human Interactions with Novel Objects from Monocular Videos

Lixin Xue, Chengwei Zheng, Georgios Paschalidis, Chen Guo, Manuel Kaufmann, Juan Zarate +1

CVPR 2026

Reconstructing people, objects, and their interactions in 3D is a long-standing and fundamental goal for intelligent systems. Often the input is RGB video from a moving camera, making the task ill-posed; depth is ambiguous, humans and objects occlude each other, and camera and object motion entangle

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2025

SDFit: 3D Object Pose and Shape by Fitting a Morphable SDF to a Single Image

Dimitrije Antić, Georgios Paschalidis, Shashank Tripathi, Theo Gevers, Sai Kumar Dwivedi, Dimitrios Tzionas

ICCV 2025poster

Recovering 3D object pose and shape from a single image is a challenging and ill-posed problem. This is due to strong (self-)occlusions, depth ambiguities, the vast intra- and inter-class shape variance, and the lack of 3D ground truth for natural images. Existing deep-network methods are trained on…

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