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Michael Paulitsch

7 accepted papers

2026

KernelFoundry: Hardware-Aware Evolutionary GPU Kernel Optimization

ICML 2026poster

GPU kernel optimization challenges LLMs beyond standard coding tasks, as it requires an understanding of hardware architecture, parallel computing optimization strategies, and profiling outputs. However, most existing approaches leveraging LLMs for kernel generation apply standard prompting and feed…

Cited by 0SourceScholar
2025

ConDo: Continual Domain Expansion for Absolute Pose Regression

AAAI 2025technical

Visual localization is a fundamental machine learning problem. Absolute Pose Regression (APR) trains a scene-dependent model to efficiently map an input image to the camera pose in a pre-defined scene. However, many applications have continually changing environments, where inference data at novel p…

2025

HoloScene: Simulation‑Ready Interactive 3D Worlds from a Single Video

NeurIPS 2025poster

Digitizing the physical world into accurate simulation‑ready virtual environments offers significant opportunities in a variety of fields such as augmented and virtual reality, gaming, and robotics. However, current 3D reconstruction and scene-understanding methods commonly fall short in one or more…

Cited by 0SourceScholar
2025

PBR-SR: Mesh PBR Texture Super Resolution from 2D Image Priors

NeurIPS 2025poster

We present PBR-SR, a novel method for physically based rendering (PBR) texture super resolution (SR). It outputs high-resolution, high-quality PBR textures from low-resolution (LR) PBR input in a zero-shot manner. PBR-SR leverages an off-the-shelf super-resolution model trained on natural images, an…

Cited by 0SourceScholar
2024

L-MAGIC: Language Model Assisted Generation of Images with Coherence

CVPR 2024poster

In the current era of generative AI breakthroughs generating panoramic scenes from a single input image remains a key challenge. Most existing methods use diffusion-based iterative or simultaneous multi-view inpainting. However the lack of global scene layout priors leads to subpar outputs with dupl…

2024

MIDGArD: Modular Interpretable Diffusion over Graphs for Articulated Designs

NeurIPS 2024poster

Providing functionality through articulation and interaction with objects is a key objective in 3D generation. We introduce MIDGArD (Modular Interpretable Diffusion over Graphs for Articulated Designs), a novel diffusion-based framework for articulated 3D asset generation. MIDGArD improves over foun…

Cited by 0SourcePDFScholar
2024

Mesh2NeRF: Direct Mesh Supervision for Neural Radiance Field Representation and Generation

ECCV 2024poster

"We present , an approach to derive ground-truth radiance fields from textured meshes for 3D generation tasks. Many 3D generative approaches represent 3D scenes as radiance fields for training. Their ground-truth radiance fields are usually fitted from multi-view renderings from a large-scale synthe…

Cited by 4SourcePDFScholar