We bring static 3D objects to life by distilling material parameters from video generation models.

Recostruct 3D physical objects from single images by considering mechanical properties, external forces, and rest-shape geometry.

We image the "ripples" on solid surfaces caused by physical impacts, which contain information about the object's physical properties and its interaction with the environment. We showcase non-line-of-sight impact localization capabilities.

We propose a real-time intermediate flow estimation (RIFE) method for video frame interpolation, it runs 30+FPS for 2X 720p interpolation on a 2080Ti GPU

In contrast to 2D, object size in 3D does not exhibit long-tail distributions. We propose a single stride sparse Transformer (SST) for 3D object detection. We obtained impressive results on small objects

A new paradigm of 3D object detection from multiview 2D images

End-to-End 3D tracking with multiview-cameras

A unified framework for 3D detection from multi-sensor data. We achieved impressive results with multiview-cameras and one-beam LiDAR.

We provide a high-quality large-scale object detection dataset, with 365 categories, 638K images, and 10,101K bounding boxes

Accelerating adversarial training using Pontryagin`s Maximum Principle

Discussion on the shape-bias and texture-bias of adversarially trainined convolutional neural networks

Reviewer: CVPR' 2021,23, NeurIPS' 2020, ICLR' 2021,22,23 BlogPosts.