46 citations · 145 across the 27 of their papers we have counts for
30 papers
3DCodeBench: Benchmarking Agentic Procedural 3D Modeling Via Code
Yipeng Gao, Lei Shu, Genzhi Ye +5
Procedural 3D modeling through code is emerging as a versatile paradigm, offering deterministic, engine-ready, and precisely editable assets that neural 3D generators inherently la…
Spectrally-Guided Diffusion Noise Schedules
Carlos Esteves, Ameesh Makadia
Denoising diffusion models are widely used for high-quality image and video generation. Their performance depends on noise schedules, which define the distribution of noise levels…
Decomposing Private Image Generation via Coarse-to-Fine Wavelet Modeling
Jasmine Bayrooti, Weiwei Kong, Natalia Ponomareva +3
Generative models trained on sensitive image datasets risk memorizing and reproducing individual training examples, making strong privacy guarantees essential. While differential p…
Spectral Image Tokenizer
Carlos Esteves, Mohammed Suhail, Ameesh Makadia
Image tokenizers map images to sequences of discrete tokens, and are a crucial component of autoregressive transformer-based image generation. The tokens are typically associated w…
Factorized Video Autoencoders for Efficient Generative Modelling
Mohammed Suhail, Carlos Esteves, Leonid Sigal +1
Latent variable generative models have emerged as powerful tools for generative tasks including image and video synthesis. These models are enabled by pretrained autoencoders that…
Single Mesh Diffusion Models with Field Latents for Texture Generation
Thomas W. Mitchel, Carlos Esteves, Ameesh Makadia
We introduce a framework for intrinsic latent diffusion models operating directly on the surfaces of 3D shapes, with the goal of synthesizing high-quality textures. Our approach is…
Learning to Transform for Generalizable Instance-wise Invariance
Utkarsh Singhal, Carlos Esteves, Ameesh Makadia +1
Computer vision research has long aimed to build systems that are robust to spatial transformations found in natural data. Traditionally, this is done using data augmentation or ha…
Scaling Spherical CNNs
Carlos Esteves, Jean-Jacques Slotine, Ameesh Makadia
Spherical CNNs generalize CNNs to functions on the sphere, by using spherical convolutions as the main linear operation. The most accurate and efficient way to compute spherical co…
LU-NeRF: Scene and Pose Estimation by Synchronizing Local Unposed NeRFs
Zezhou Cheng, Carlos Esteves, Varun Jampani +3
A critical obstacle preventing NeRF models from being deployed broadly in the wild is their reliance on accurate camera poses. Consequently, there is growing interest in extending…
NAVI: Category-Agnostic Image Collections with High-Quality 3D Shape and Pose Annotations
Varun Jampani, Kevis-Kokitsi Maninis, Andreas Engelhardt +13
Recent advances in neural reconstruction enable high-quality 3D object reconstruction from casually captured image collections. Current techniques mostly analyze their progress on…
ASIC: Aligning Sparse in-the-wild Image Collections
Kamal Gupta, Varun Jampani, Carlos Esteves +4
We present a method for joint alignment of sparse in-the-wild image collections of an object category. Most prior works assume either ground-truth keypoint annotations or a large d…
Stable Object Reorientation using Contact Plane Registration
Richard Li, Carlos Esteves, Ameesh Makadia +1
We present a system for accurately predicting stable orientations for diverse rigid objects. We propose to overcome the critical issue of modelling multimodality in the space of ro…
Generalizable Patch-Based Neural Rendering
Mohammed Suhail, Carlos Esteves, Leonid Sigal +1
Neural rendering has received tremendous attention since the advent of Neural Radiance Fields (NeRF), and has pushed the state-of-the-art on novel-view synthesis considerably. The…
Light Field Neural Rendering
Mohammed Suhail, Carlos Esteves, Leonid Sigal +1
Classical light field rendering for novel view synthesis can accurately reproduce view-dependent effects such as reflection, refraction, and translucency, but requires a dense view…
Wide-Baseline Relative Camera Pose Estimation with Directional Learning
Kefan Chen, Noah Snavely, Ameesh Makadia
Modern deep learning techniques that regress the relative camera pose between two images have difficulty dealing with challenging scenarios, such as large camera motions resulting…
Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifold
Kieran Murphy, Carlos Esteves, Varun Jampani +2
Single image pose estimation is a fundamental problem in many vision and robotics tasks, and existing deep learning approaches suffer by not completely modeling and handling: i) un…
KeypointDeformer: Unsupervised 3D Keypoint Discovery for Shape Control
Tomas Jakab, Richard Tucker, Ameesh Makadia +3
We introduce KeypointDeformer, a novel unsupervised method for shape control through automatically discovered 3D keypoints. We cast this as the problem of aligning a source 3D obje…
De-rendering the World's Revolutionary Artefacts
Shangzhe Wu, Ameesh Makadia, Jiajun Wu +3
Recent works have shown exciting results in unsupervised image de-rendering -- learning to decompose 3D shape, appearance, and lighting from single-image collections without explic…
Learning ABCs: Approximate Bijective Correspondence for isolating factors of variation with weak supervision
Kieran A. Murphy, Varun Jampani, Srikumar Ramalingam +1
Representational learning forms the backbone of most deep learning applications, and the value of a learned representation is intimately tied to its information content regarding d…
Infinite Nature: Perpetual View Generation of Natural Scenes from a Single Image
Andrew Liu, Richard Tucker, Varun Jampani +3
We introduce the problem of perpetual view generation - long-range generation of novel views corresponding to an arbitrarily long camera trajectory given a single image. This is a…
An Analysis of SVD for Deep Rotation Estimation
Jake Levinson, Carlos Esteves, Kefan Chen +4
Symmetric orthogonalization via SVD, and closely related procedures, are well-known techniques for projecting matrices onto or . These tools have long been used for a…
Spin-Weighted Spherical CNNs
Carlos Esteves, Ameesh Makadia, Kostas Daniilidis
Learning equivariant representations is a promising way to reduce sample and model complexity and improve the generalization performance of deep neural networks. The spherical CNNs…
Time Dependence in Non-Autonomous Neural ODEs
Jared Quincy Davis, Krzysztof Choromanski, Jake Varley +6
Neural Ordinary Differential Equations (ODEs) are elegant reinterpretations of deep networks where continuous time can replace the discrete notion of depth, ODE solvers perform for…
Local Implicit Grid Representations for 3D Scenes
Chiyu Max Jiang, Avneesh Sud, Ameesh Makadia +3
Shape priors learned from data are commonly used to reconstruct 3D objects from partial or noisy data. Yet no such shape priors are available for indoor scenes, since typical 3D au…