2 citations · 7 across the 14 of their papers we have counts for
15 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…
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,…
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…
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…
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…
GAS-NeRF: Geometry-Aware Stylization of Dynamic Radiance Fields
Nhat Phuong Anh Vu, Abhishek Saroha, Or Litany +1
Current 3D stylization techniques primarily focus on static scenes, while our world is inherently dynamic, filled with moving objects and changing environments. Existing style tran…
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…
DiffCD: A Symmetric Differentiable Chamfer Distance for Neural Implicit Surface Fitting
Linus Härenstam-Nielsen, Lu Sang, Abhishek Saroha +2
Neural implicit surfaces can be used to recover accurate 3D geometry from imperfect point clouds. In this work, we show that state-of-the-art techniques work by minimizing an appro…
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…
ResolvNet: A Graph Convolutional Network with multi-scale Consistency
Christian Koke, Abhishek Saroha, Yuesong Shen +2
It is by now a well known fact in the graph learning community that the presence of bottlenecks severely limits the ability of graph neural networks to propagate information over l…
Enhancing Surface Neural Implicits with Curvature-Guided Sampling and Uncertainty-Augmented Representations
Lu Sang, Abhishek Saroha, Maolin Gao +1
Neural implicit representations have become a popular choice for modeling surfaces due to their adaptability in resolution and support for complex topology. While previous works ha…
Implicit Shape Completion via Adversarial Shape Priors
Abhishek Saroha, Marvin Eisenberger, Tarun Yenamandra +1
We present a novel neural implicit shape method for partial point cloud completion. To that end, we combine a conditional Deep-SDF architecture with learned, adversarial shape prio…
FPGA Implementation of Minimum Mean Brightness Error Bi-Histogram Equalization
Abhishek Saroha, Avichal Rakesh, Rajiv Kumar Tripathi
Histogram Equalization (HE) is a popular method for contrast enhancement. Generally, mean brightness is not conserved in Histogram Equalization. Initially, Bi-Histogram Equalizatio…
Efficient Edge Rewiring Strategies for Enhancement in Network Capacity
Suchi Kumari, Abhishek Saroha, Anurag Singh
The structure of the network has great impact on its traffic dynamics. Most of the real world networks follow the heterogeneous structure and exhibit scale-free feature. In scale-f…