422 citations · 927 across the 15 of their papers we have counts for
18 papers
LoGo: Local-Global Rewards for Consistent Long-Horizon Video Generation
Ziqi Ma, Shreya Sharma, Mohamed El Banani +8
Camera-controlled video models are rapidly advancing toward long generation horizons and complex camera control. A key failure mode is 3D inconsistency: as the camera moves, object…
SAM 3D: 3Dfy Anything in Images
SAM 3D Team, Xingyu Chen, Fu-Jen Chu +20
We present SAM 3D, a generative model for visually grounded 3D object reconstruction, predicting geometry, texture, and layout from a single image. SAM 3D excels in natural images,…
Reconstructing Hand-Held Objects in 3D from Images and Videos
Jane Wu, Georgios Pavlakos, Georgia Gkioxari +1
Objects manipulated by the hand (i.e., manipulanda) are particularly challenging to reconstruct from Internet videos. Not only does the hand occlude much of the object, but also th…
Multiview Compressive Coding for 3D Reconstruction
Chao-Yuan Wu, Justin Johnson, Jitendra Malik +2
A central goal of visual recognition is to understand objects and scenes from a single image. 2D recognition has witnessed tremendous progress thanks to large-scale learning and ge…
Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild
Garrick Brazil, Abhinav Kumar, Julian Straub +3
Recognizing scenes and objects in 3D from a single image is a longstanding goal of computer vision with applications in robotics and AR/VR. For 2D recognition, large datasets and s…
Learning 3D Object Shape and Layout without 3D Supervision
Georgia Gkioxari, Nikhila Ravi, Justin Johnson
A 3D scene consists of a set of objects, each with a shape and a layout giving their position in space. Understanding 3D scenes from 2D images is an important goal, with applicatio…
Recognizing Scenes from Novel Viewpoints
Shengyi Qian, Alexander Kirillov, Nikhila Ravi +4
Humans can perceive scenes in 3D from a handful of 2D views. For AI agents, the ability to recognize a scene from any viewpoint given only a few images enables them to efficiently…
Differentiable Stereopsis: Meshes from multiple views using differentiable rendering
Shubham Goel, Georgia Gkioxari, Jitendra Malik
We propose Differentiable Stereopsis, a multi-view stereo approach that reconstructs shape and texture from few input views and noisy cameras. We pair traditional stereopsis and mo…
Accelerating 3D Deep Learning with PyTorch3D
Nikhila Ravi, Jeremy Reizenstein, David Novotny +4
Deep learning has significantly improved 2D image recognition. Extending into 3D may advance many new applications including autonomous vehicles, virtual and augmented reality, aut…
3D Shape Reconstruction from Vision and Touch
Edward J. Smith, Roberto Calandra, Adriana Romero +4
When a toddler is presented a new toy, their instinctual behaviour is to pick it upand inspect it with their hand and eyes in tandem, clearly searching over its surface to properly…
SynSin: End-to-end View Synthesis from a Single Image
Olivia Wiles, Georgia Gkioxari, Richard Szeliski +1
Single image view synthesis allows for the generation of new views of a scene given a single input image. This is challenging, as it requires comprehensively understanding the 3D s…
Data Distillation: Towards Omni-Supervised Learning
Ilija Radosavovic, Piotr Dollár, Ross Girshick +2
We investigate omni-supervised learning, a special regime of semi-supervised learning in which the learner exploits all available labeled data plus internet-scale sources of unlabe…
Detecting and Recognizing Human-Object Interactions
Georgia Gkioxari, Ross Girshick, Piotr Dollár +1
To understand the visual world, a machine must not only recognize individual object instances but also how they interact. Humans are often at the center of such interactions and de…
Mask R-CNN
Kaiming He, Georgia Gkioxari, Piotr Dollár +1
We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously genera…
Contextual Action Recognition with R*CNN
Georgia Gkioxari, Ross Girshick, Jitendra Malik
There are multiple cues in an image which reveal what action a person is performing. For example, a jogger has a pose that is characteristic for jogging, but the scene (e.g. road,…
Actions and Attributes from Wholes and Parts
Georgia Gkioxari, Ross Girshick, Jitendra Malik
We investigate the importance of parts for the tasks of action and attribute classification. We develop a part-based approach by leveraging convolutional network features inspired…
Finding Action Tubes
Georgia Gkioxari, Jitendra Malik
We address the problem of action detection in videos. Driven by the latest progress in object detection from 2D images, we build action models using rich feature hierarchies derive…
R-CNNs for Pose Estimation and Action Detection
Georgia Gkioxari, Bharath Hariharan, Ross Girshick +1
We present convolutional neural networks for the tasks of keypoint (pose) prediction and action classification of people in unconstrained images. Our approach involves training an…