1 citations · 2 across the 6 of their papers we have counts for
6 papers
EquiFusion: Kinematics-Agnostic Human Motion Prediction via Equivariant Latent Diffusion
Cecilia Curreli, Florian Hofherr, Dominik Muhle +3
Existing Stochastic 3D Human Motion Prediction models are fundamentally constrained by hard-coding the skeleton kinematics, severely limiting generalization, preventing cross-datas…
Benchmarking Single-Step Inpainting Methods for Multi-Object 3D Gaussian Splatting Scenes
Finn Dröge, Cecilia Curreli, Abhishek Saroha +1
The tasks of object removal and inpainting 3D Gaussian Splatting (3DGS) scenes face challenges such as 3D consistency across camera views. In comparing 2D inpainters and their suit…
Prototype Augmented Hypernetworks for Continual Learning
Neil De La Fuente, Maria Pilligua, Daniel Vidal +4
Continual learning (CL) aims to learn a sequence of tasks without forgetting prior knowledge, but gradient updates for a new task often overwrite the weights learned earlier, causi…
ZDySS -- Zero-Shot Dynamic Scene Stylization using Gaussian Splatting
Abhishek Saroha, Florian Hofherr, Mariia Gladkova +3
Stylizing a dynamic scene based on an exemplar image is critical for various real-world applications, including gaming, filmmaking, and augmented and virtual reality. However, achi…
Nonisotropic Gaussian Diffusion for Realistic 3D Human Motion Prediction
Cecilia Curreli, Dominik Muhle, Abhishek Saroha +3
Probabilistic human motion prediction aims to forecast multiple possible future movements from past observations. While current approaches report high diversity and realism, they o…
Gaussian Splatting in Style
Abhishek Saroha, Mariia Gladkova, Cecilia Curreli +3
3D scene stylization extends the work of neural style transfer to 3D. A vital challenge in this problem is to maintain the uniformity of the stylized appearance across multiple vie…