216 citations · 351 across the 16 of their papers we have counts for
16 papers
Large-scale Codec Avatars: The Unreasonable Effectiveness of Large-scale Avatar Pretraining
Junxuan Li, Rawal Khirodkar, Chengan He +37
High-quality 3D avatar modeling faces a critical trade-off between fidelity and generalization. On the one hand, multi-view studio data enables high-fidelity modeling of humans wit…
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…
The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation
Saurabh Saxena, Charles Herrmann, Junhwa Hur +4
Denoising diffusion probabilistic models have transformed image generation with their impressive fidelity and diversity. We show that they also excel in estimating optical flow and…
Unsupervised Semantic Correspondence Using Stable Diffusion
Eric Hedlin, Gopal Sharma, Shweta Mahajan +4
Text-to-image diffusion models are now capable of generating images that are often indistinguishable from real images. To generate such images, these models must understand the sem…
: Dual-Camera Defocus Control by Learning to Refocus
Hadi Alzayer, Abdullah Abuolaim, Leung Chun Chan +4
Smartphone cameras today are increasingly approaching the versatility and quality of professional cameras through a combination of hardware and software advancements. However, fixe…
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…
Monocular Depth Estimation using Diffusion Models
Saurabh Saxena, Abhishek Kar, Mohammad Norouzi +1
We formulate monocular depth estimation using denoising diffusion models, inspired by their recent successes in high fidelity image generation. To that end, we introduce innovation…
SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image collections
Mark Boss, Andreas Engelhardt, Abhishek Kar +5
Inverse rendering of an object under entirely unknown capture conditions is a fundamental challenge in computer vision and graphics. Neural approaches such as NeRF have achieved ph…
Learned Monocular Depth Priors in Visual-Inertial Initialization
Yunwen Zhou, Abhishek Kar, Eric Turner +4
Visual-inertial odometry (VIO) is the pose estimation backbone for most AR/VR and autonomous robotic systems today, in both academia and industry. However, these systems are highly…
SLIDE: Single Image 3D Photography with Soft Layering and Depth-aware Inpainting
Varun Jampani, Huiwen Chang, Kyle Sargent +8
Single image 3D photography enables viewers to view a still image from novel viewpoints. Recent approaches combine monocular depth networks with inpainting networks to achieve comp…
Learning Independent Object Motion from Unlabelled Stereoscopic Videos
Zhe Cao, Abhishek Kar, Christian Haene +1
We present a system for learning motion of independently moving objects from stereo videos. The only human annotation used in our system are 2D object bounding boxes which introduc…
Learning a Multi-View Stereo Machine
Abhishek Kar, Christian Häne, Jitendra Malik
We present a learnt system for multi-view stereopsis. In contrast to recent learning based methods for 3D reconstruction, we leverage the underlying 3D geometry of the problem thro…
Shape and Symmetry Induction for 3D Objects
Shubham Tulsiani, Abhishek Kar, Qixing Huang +2
Actions as simple as grasping an object or navigating around it require a rich understanding of that object's 3D shape from a given viewpoint. In this paper we repurpose powerful l…
Amodal Completion and Size Constancy in Natural Scenes
Abhishek Kar, Shubham Tulsiani, João Carreira +1
We consider the problem of enriching current object detection systems with veridical object sizes and relative depth estimates from a single image. There are several technical chal…
Virtual View Networks for Object Reconstruction
João Carreira, Abhishek Kar, Shubham Tulsiani +1
All that structure from motion algorithms "see" are sets of 2D points. We show that these impoverished views of the world can be faked for the purpose of reconstructing objects in…
Category-Specific Object Reconstruction from a Single Image
Abhishek Kar, Shubham Tulsiani, João Carreira +1
Object reconstruction from a single image -- in the wild -- is a problem where we can make progress and get meaningful results today. This is the main message of this paper, which…