23 citations · 66 across the 12 of their papers we have counts for
12 papers
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…
Learning Signal-Agnostic Manifolds of Neural Fields
Yilun Du, Katherine M. Collins, Joshua B. Tenenbaum +1
Deep neural networks have been used widely to learn the latent structure of datasets, across modalities such as images, shapes, and audio signals. However, existing models are gene…
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…