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From the 1 of 8 linked papers with an AI index.

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8 papers

cs.CV2026

EquiFusion: Kinematics-Agnostic Human Motion Prediction via Equivariant Latent Diffusion

Cecilia Curreli, Florian Hofherr, Dominik Muhle +3

EquiFusion is a latent diffusion model for 3D human motion prediction that does not rely on fixed skeleton kinematics, allowing it to generalize across datasets and handle partial…

cs.LG2026

Graph Neural Networks Are Not Continuous Across Graph Resolutions

Christian Koke, Yuesong Shen, Abhishek Saroha +4

We show that contrary to conventional wisdom in the community, graph neural networks (GNNs) are not continuous with respect to all natural modes of graph convergence. As a result,…

cs.CV2026

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…

cs.CV2026

EgoFlow: Gradient-Guided Flow Matching for Egocentric 6DoF Object Motion Generation

Abhishek Saroha, Huajian Zeng, Xingxing Zuo +2

Understanding and predicting object motion from egocentric video is fundamental to embodied perception and interaction. However, generating physically consistent 6DoF trajectories…

cs.CV2026

GMT: Goal-Conditioned Multimodal Transformer for 6-DOF Object Trajectory Synthesis in 3D Scenes

Huajian Zeng, Abhishek Saroha, Daniel Cremers +1

Synthesizing controllable 6-DOF object manipulation trajectories in 3D environments is essential for enabling robots to interact with complex scenes, yet remains challenging due to…

cs.CV2025

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…