69 citations · 77 across the 16 of their papers we have counts for
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3D Spatial Understanding in MLLMs: Disambiguation and Evaluation
Chun-Peng Chang, Alain Pagani, Didier Stricker
Multimodal Large Language Models (MLLMs) have made significant progress in tasks such as image captioning and question answering. However, while these models can generate realistic…
Uni-SLAM: Uncertainty-Aware Neural Implicit SLAM for Real-Time Dense Indoor Scene Reconstruction
Shaoxiang Wang, Yaxu Xie, Chun-Peng Chang +3
Neural implicit fields have recently emerged as a powerful representation method for multi-view surface reconstruction due to their simplicity and state-of-the-art performance. How…
G3FA: Geometry-guided GAN for Face Animation
Alireza Javanmardi, Alain Pagani, Didier Stricker
Animating human face images aims to synthesize a desired source identity in a natural-looking way mimicking a driving video's facial movements. In this context, Generative Adversar…
SG-PGM: Partial Graph Matching Network with Semantic Geometric Fusion for 3D Scene Graph Alignment and Its Downstream Tasks
Yaxu Xie, Alain Pagani, Didier Stricker
Scene graphs have been recently introduced into 3D spatial understanding as a comprehensive representation of the scene. The alignment between 3D scene graphs is the first step of…
MiKASA: Multi-Key-Anchor & Scene-Aware Transformer for 3D Visual Grounding
Chun-Peng Chang, Shaoxiang Wang, Alain Pagani +1
3D visual grounding involves matching natural language descriptions with their corresponding objects in 3D spaces. Existing methods often face challenges with accuracy in object re…