23 citations · 66 across the 12 of their papers we have counts for
11 papers · 1 filter
MilliVid: Hierarchical Latents for Long-Range Consistency in Video Generation
Ishaan Preetam Chandratreya, David Charatan, Basile Van Hoorick +4
Video generative models have become increasingly powerful, but long-range consistency remains challenging to achieve because even a few dozen frames require impractically long tran…
Understanding Multi-View Transformers
Michal Stary, Julien Gaubil, Ayush Tewari +1
Multi-view transformers such as DUSt3R are revolutionizing 3D vision by solving 3D tasks in a feed-forward manner. However, contrary to previous optimization-based pipelines, the i…
FlowMap: High-Quality Camera Poses, Intrinsics, and Depth via Gradient Descent
Cameron Smith, David Charatan, Ayush Tewari +1
This paper introduces FlowMap, an end-to-end differentiable method that solves for precise camera poses, camera intrinsics, and per-frame dense depth of a video sequence. Our metho…
Approaching human 3D shape perception with neurally mappable models
Thomas P. O'Connell, Tyler Bonnen, Yoni Friedman +4
Humans effortlessly infer the 3D shape of objects. What computations underlie this ability? Although various computational models have been proposed, none of them capture the human…
Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct Supervision
Ayush Tewari, Tianwei Yin, George Cazenavette +5
Denoising diffusion models are a powerful type of generative models used to capture complex distributions of real-world signals. However, their applicability is limited to scenario…
FlowCam: Training Generalizable 3D Radiance Fields without Camera Poses via Pixel-Aligned Scene Flow
Cameron Smith, Yilun Du, Ayush Tewari +1
Reconstruction of 3D neural fields from posed images has emerged as a promising method for self-supervised representation learning. The key challenge preventing the deployment of t…
Learning to Render Novel Views from Wide-Baseline Stereo Pairs
Yilun Du, Cameron Smith, Ayush Tewari +1
We introduce a method for novel view synthesis given only a single wide-baseline stereo image pair. In this challenging regime, 3D scene points are regularly observed only once, re…
DeLiRa: Self-Supervised Depth, Light, and Radiance Fields
Vitor Guizilini, Igor Vasiljevic, Jiading Fang +4
Differentiable volumetric rendering is a powerful paradigm for 3D reconstruction and novel view synthesis. However, standard volume rendering approaches struggle with degenerate ge…
Neural Groundplans: Persistent Neural Scene Representations from a Single Image
Prafull Sharma, Ayush Tewari, Yilun Du +7
We present a method to map 2D image observations of a scene to a persistent 3D scene representation, enabling novel view synthesis and disentangled representation of the movable an…
Unsupervised Discovery and Composition of Object Light Fields
Cameron Smith, Hong-Xing Yu, Sergey Zakharov +4
Neural scene representations, both continuous and discrete, have recently emerged as a powerful new paradigm for 3D scene understanding. Recent efforts have tackled unsupervised di…
Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering
Vincent Sitzmann, Semon Rezchikov, William T. Freeman +2
Inferring representations of 3D scenes from 2D observations is a fundamental problem of computer graphics, computer vision, and artificial intelligence. Emerging 3D-structured neur…