[CVPR 2022--Oral] Restormer: Efficient Transformer for High-Resolution Image Restoration. SOTA for motion deblurring, image deraining, denoising (Gaussian/real data), and defocus deblurring.

Official PyTorch Code and Models of "RePaint: Inpainting using Denoising Diffusion Probabilistic Models", CVPR 2022

Point-NeRF: Point-based Neural Radiance Fields

The official implementation of [CVPR2022] Decoupled Knowledge Distillation https://arxiv.org/abs/2203.08679 and [ICCV2023] DOT: A Distillation-Oriented Trainer https://openaccess.thecvf.com/content/ICCV2023/papers/Zhao_DOT_A_Distillation-Oriented_Trainer_ICCV_2023_paper.pdf

[CVPR 2022] HiVT: Hierarchical Vector Transformer for Multi-Agent Motion Prediction

Official PyTorch Implementation of "GAN-Supervised Dense Visual Alignment" (CVPR 2022 Oral, Best Paper Finalist)

Official Implementation for "HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing" (CVPR 2022) https://arxiv.org/abs/2111.15666

Point-to-Voxel Knowledge Distillation for LiDAR Semantic Segmentation (CVPR 2022)

Direct voxel grid optimization for fast radiance field reconstruction.

Official code for "Towards An End-to-End Framework for Flow-Guided Video Inpainting" (CVPR2022)

[CVPR22] Official Implementation of DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation

HumanNeRF turns a monocular video of moving people into a 360 free-viewpoint video.

Cross-view Transformers for real-time Map-view Semantic Segmentation (CVPR 2022 Oral)

[CVPR 2022 Oral & TPAMI 2024] MixFormer: End-to-End Tracking with Iterative Mixed Attention

Solution for Waymo Motion Prediction Challenge 2022. Our implementation of MultiPath++

(CVPR 2022) A minimalist, mapless, end-to-end self-driving stack for joint perception, prediction, planning and control.

[CVPR 2022] "MonoScene: Monocular 3D Semantic Scene Completion": 3D Semantic Occupancy Prediction from a single image

Mask Transfiner for High-Quality Instance Segmentation, CVPR 2022

[CVPR'22 & IJCV'24] Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels & Using Unreliable Pseudo-Labels for Label-Efficient Semantic Segmentation