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20242026
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cs.CV2026

Fillerbuster: Unified Generative Scene Completion Model for Casual Captures

Ethan Weber, Norman Müller, Yash Kant +4

We present Fillerbuster, a unified model that completes unknown regions of a 3D scene with a multi-view latent diffusion transformer. Casual captures are often sparse and miss surr…

cs.CV2025

FlowR: Flowing from Sparse to Dense 3D Reconstructions

Tobias Fischer, Samuel Rota Bulò, Yung-Hsu Yang +7

3D Gaussian splatting enables high-quality novel view synthesis (NVS) at real-time frame rates. However, its quality drops sharply as we depart from the training views. Thus, dense…

cs.CV2024

Multi-view Image Diffusion via Coordinate Noise and Fourier Attention

Justin Theiss, Norman Müller, Daeil Kim +1

Recently, text-to-image generation with diffusion models has made significant advancements in both higher fidelity and generalization capabilities compared to previous baselines. H…

cs.CV2024

ViewDiff: 3D-Consistent Image Generation with Text-to-Image Models

Lukas Höllein, Aljaž Božič, Norman Müller +5

3D asset generation is getting massive amounts of attention, inspired by the recent success of text-guided 2D content creation. Existing text-to-3D methods use pretrained text-to-i…

cs.CV2024

Surf-D: Generating High-Quality Surfaces of Arbitrary Topologies Using Diffusion Models

Zhengming Yu, Zhiyang Dou, Xiaoxiao Long +9

We present Surf-D, a novel method for generating high-quality 3D shapes as Surfaces with arbitrary topologies using Diffusion models. Previous methods explored shape generation wit…