most citedDeep Isometric Learning for Visual Recognition

24 citations · 50 across the 5 of their papers we have counts for

collaborators

12 papers

cs.CV20206 cited

Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wild

Jason Y. Zhang, Sam Pepose, Hanbyul Joo +3

We present a method that infers spatial arrangements and shapes of humans and objects in a globally consistent 3D scene, all from a single image in-the-wild captured in an uncontro…

cs.CV20208 cited

Long-term Human Motion Prediction with Scene Context

Zhe Cao, Hang Gao, Karttikeya Mangalam +3

Human movement is goal-directed and influenced by the spatial layout of the objects in the scene. To plan future human motion, it is crucial to perceive the environment -- imagine…

cs.CV2020

Shape and Viewpoint without Keypoints

Shubham Goel, Angjoo Kanazawa, Jitendra Malik

We present a learning framework that learns to recover the 3D shape, pose and texture from a single image, trained on an image collection without any ground truth 3D shape, multi-v…

cs.CV2020

Robust Learning Through Cross-Task Consistency

Amir Zamir, Alexander Sax, Teresa Yeo +6

Visual perception entails solving a wide set of tasks, e.g., object detection, depth estimation, etc. The predictions made for multiple tasks from the same image are not independen…

cs.CV202024 cited

Deep Isometric Learning for Visual Recognition

Haozhi Qi, Chong You, Xiaolong Wang +2

Initialization, normalization, and skip connections are believed to be three indispensable techniques for training very deep convolutional neural networks and obtaining state-of-th…

cs.CV2020

Multimodal Image Synthesis with Conditional Implicit Maximum Likelihood Estimation

Ke Li, Shichong Peng, Tianhao Zhang +1

Many tasks in computer vision and graphics fall within the framework of conditional image synthesis. In recent years, generative adversarial nets (GANs) have delivered impressive a…