173 citations · 174 across the 8 of their papers we have counts for
10 papers
InstructMesh: Selective Refinement of Generative 3D Models for Fabrication
Faraz Faruqi, Ahmed Katary, Demircan Tas +10
Recent advances in generative AI allow users to create 3D models from text or images. However, these models prioritize visual plausibility over geometric accuracy, often generating…
Elastic3D: Controllable Stereo Video Conversion with Guided Latent Decoding
Nando Metzger, Prune Truong, Goutam Bhat +2
The growing demand for immersive 3D content calls for automated monocular-to-stereo video conversion. We present Elastic3D, a controllable, direct end-to-end method for upgrading a…
Understanding, Accelerating, and Improving MeanFlow Training
Jin-Young Kim, Hyojun Go, Lea Bogensperger +5
MeanFlow promises high-quality generative modeling in few steps, by jointly learning instantaneous and average velocity fields. Yet, the underlying training dynamics remain unclear…
Text-to-3D by Stitching a Multi-view Reconstruction Network to a Video Generator
Hyojun Go, Dominik Narnhofer, Goutam Bhat +3
The rapid progress of large, pretrained models for both visual content generation and 3D reconstruction opens up new possibilities for text-to-3D generation. Intuitively, one could…
M2SVid: End-to-End Inpainting and Refinement for Monocular-to-Stereo Video Conversion
Nina Shvetsova, Goutam Bhat, Prune Truong +2
We tackle the problem of monocular-to-stereo video conversion and propose a novel architecture for inpainting and refinement of the warped right view obtained by depth-based reproj…
PRISM: Probabilistic Representation for Integrated Shape Modeling and Generation
Lei Cheng, Mahdi Saleh, Qing Cheng +4
Despite the advancements in 3D full-shape generation, accurately modeling complex geometries and semantics of shape parts remains a significant challenge, particularly for shapes w…
Gatekeeper: Improving Model Cascades Through Confidence Tuning
Stephan Rabanser, Nathalie Rauschmayr, Achin Kulshrestha +5
Large-scale machine learning models deliver strong performance across a wide range of tasks but come with significant computational and resource constraints. To mitigate these chal…
CubeDiff: Repurposing Diffusion-Based Image Models for Panorama Generation
Nikolai Kalischek, Michael Oechsle, Fabian Manhardt +3
We introduce a novel method for generating 360° panoramas from text prompts or images. Our approach leverages recent advances in 3D generation by employing multi-view diffusion mod…
Extracting Training Data from Document-Based VQA Models
Francesco Pinto, Nathalie Rauschmayr, Florian Tramèr +2
Vision-Language Models (VLMs) have made remarkable progress in document-based Visual Question Answering (i.e., responding to queries about the contents of an input document provide…
Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language
Andy Zeng, Maria Attarian, Brian Ichter +10
Large pretrained (e.g., "foundation") models exhibit distinct capabilities depending on the domain of data they are trained on. While these domains are generic, they may only barel…