June 18–22, 2023

At the Computer Vision and Pattern Recognition (CVPR) online conference, NVIDIA researchers shared their latest groundbreaking innovations—including forty eight papers. Explore the work presented and see how NVIDIA Research collaborates with CVPR members to deliver AI breakthroughs across the community.

NKSR Animation

NVIDIA NKSR Sweepstakes

Neural Kernel Surface Reconstruction (NKSR), a CVPR entry, is encouraging researchers, developers, and enthusiasts to try out the model for a chance to win an NVIDIA RTX™ 3090Ti .

NVIDIA Research Team Wins First Place and Innovation Award for Autonomous Driving Challenge at CVPR

NVIDIA Research Papers at CVPR 2023

NVIDIA’s accepted papers at CVPR 2023 feature a range of groundbreaking research in the field of computer vision. From human motion forecasting to extracting triangular 3D models, materials, and lighting from images, explore the work NVIDIA brought to the CVPR community.

* Denotes equal contribution to the paper.

Magic3D: High-Resolution Text-to-3D Content Creation

Chen-Hsuan Lin*, Jun Gao*, Luming Tang*, Towaki Takikawa*, Xiaohui Zeng*, Xun Huang, Karsten Kreis, Sanja Fidler*, Ming-Yu Liu*, Tsung-Yi Lin | Paper

Align Your Latents: High-Resolution Video Synthesis With Latent Diffusion Models

Andreas Blattmann*, Robin Rombach*, Huan Ling*, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, Karsten Kreis | Paper

Neural Kernel Surface Reconstruction

Jiahui Huang, Zan Gojcic, Matan Atzmon, Or Litany, Sanja Fidler, Francis Williams | Paper

Affordance Diffusion: Synthesizing Hand-Object Interactions

Yufei Ye, Xueting Li, Abhinav Gupta, Shalini De Mello, Stan Birchfield, Jiaming Song, Shubham Tulsiani, Sifei Liu | Paper

Neuralangelo: High-Fidelity Neural Surface Reconstruction

Zhaoshuo Li, Thomas Muller, Alex Evans, Russell H. Taylor, Mathias Unberath | Paper

Zero-Shot Pose Transfer for Unrigged Stylized 3D Characters

Jiashun Wang, Xueting Li, Sifei Liu, Shalini De Mello, Orazio Gallo, Xiaolong Wang, Jan Kautz | Paper

The Best Defense Is a Good Offense: Adversarial Augmentation Against Adversarial Attacks

Iuri Frosio, Jan Kautz | Paper

DiffCollage: Parallel Generation of Large Content With Diffusion Models

Qinsheng Zhang, Jiaming Song, Xun Huang, Yongxin Chen, Ming-Yu Liu | Paper

FreeNeRF: Improving Few-Shot Neural Rendering With Free Frequency Regularization

Jiawei Yang, Marco Pavone, and Yue Wang | Paper

Vision Transformers Are Good Mask Auto-Labelers

Shiyi Lan, Xitong Yang, Zhiding Yu, Zuxuan Wu, Jose M. Alvarez, Anima Anandkumar | Paper

VoxFormer: Sparse Voxel Transformer for Camera-Based 3D Semantic Scene Completion

Yiming Li, Zhiding Yu, Chris Choy, Chaowei Xiao, Jose M. Alvarez, Sanja Fidler, Chen Feng, Anima Anandkumar | Paper

Open-Vocabulary Panoptic Segmentation With Text-to-Image Diffusion Models

Jiarui Xu, Sifei Liu, Arash Vahdat, Wonmin Byeon, Xiaolong Wang, Shalini De Mello | Paper

GazeNeRF: 3D-Aware Gaze Redirection With Neural Radiance Fields

Alessandro Ruzzi, Xiangwei Shi, Xi Wang, Gengyan Li, Shalini De Mello, Hyung Jin Chang, Xucong Zhang, Otmar Hilliges | Paper

Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion

Davis Rempe, Zhengyi Luo, Xue Bin Peng, Ye Yuan, Kris M. Kitani, Karsten Kreis, Sanja Fidler, Or Litany | Paper

Heterogeneous Continual Learning

Divyam Madaan, Hongxu Yin, Wonmin Byeon, Jan Kautz, Pavlo Molchanov | Paper

Global Vision Transformer Pruning With Hessian-Aware Saliency

Huanrui Yang, Hongxu Yin, Maying Shen, Pavlo Molchanov, Hai Li, Jan Kautz | Paper

Recurrence Without Recurrence: Stable Video Landmark Detection With Deep Equilibrium Models

Paul Micaelli, Pavlo Molchanov, Arash Vahdat, Hongxu Yin, Jan Kautz | Paper

Fast Monocular Scene Reconstruction With Global-Sparse Local-Dense Grids

Wei Dong, Chris Choy, Charles Loop, Or Litany, Yuke Zhu | Paper

TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimation

Taeyeop Lee, Jonathan Tremblay, Valts Blukis, Bowen Wen, Byeong-Uk Lee, Inkyu Shin, Stan Birchfield, In So Kweon, Kuk-Jin Yoon | Paper

NeuralField-LDM: Scene Generation With Hierarchical Latent Diffusion Models

Seung Wook Kim*, Bradley Brown*, Kangxue Yin, Karsten Kreis, Katja Schwarz, Daiqing Li, Robin Rombach, Antonio Torralba, Sanja Fidler | Paper

Neural Fields Meet Explicit Geometric Representations for Inverse Rendering of Urban Scenes

Zian Wang, Tianchang Shen, Jun Gao, Shengyu Huang, Jacob Munkberg, Jon Hasselgren, Zan Gojcic, Wenzheng Chen, Sanja Fidler | Paper

Learning 3D Reconstruction Network From 2D StyleGAN

Aysegul Dundar, Jun Gao, Andrew Tao, Bryan Catanzaro | Paper

Learning Human-to-Robot Handovers From Point Clouds

Sammy Christen, Wei Yang, Claudia Pérez-D'Arpino, Otmar Hilliges, Dieter Fox, Yu-Wei Chao | Paper

Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects

Bowen Wen, Jonathan Tremblay, Valts Blukis, Stephen Tyree, Thomas Müller, Alex Evans, Dieter Fox, Jan Kautz, Stan Birchfield | Paper

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Deep Dive

Synthetic Data for Training and Validating Computer Vision Systems

Rendered.ai’s introductory tutorial shows how to create, use, and iterate on synthetic data with the open Rendered.ai platform and features a demonstration of NVIDIA Omniverse™ Replicator in the AWS cloud. 

Image courtesy of Microsoft Bing

Increasing Throughput and Reducing Cost for AI-Based Computer Vision With CV-CUDA

CV-CUDA® open beta optimizes pre- and postprocessing for an end-to-end throughput improvement of up to 49X over CPU. CV-CUDA enables real-time, high-performance, cloud-scale applications that demand lower latency and higher throughput.

Deep Imagination Research

NVIDIA Deep Imagination Research Group consists of a group of researchers passionate about deep generative models and their applications in content creation.

NVIDIA Instant NeRF capture of a lighthouse.


NVIDIA Instant NeRF is software that uses AI and neural radiance fields (NeRF) to approximate how light behaves in the real world, enabling users to reconstruct a 3D scene from a handful of images taken at different angles in a matter of seconds.

Featured Demos

Explore how NVIDIA technologies are transforming a variety of industries with powerful demos that highlight the latest breakthroughs in AI, data science, graphics, healthcare, and more.

NVIDIA Picasso Service

Explore Picasso, a cloud-based generative AI solution for creating images, videos, and 3D applications.


See how Runway is optimizing AI image and video generation tools using CV-CUDA.

NVIDIA NeMo Service

Learn how NVIDIA NeMo™ service can boost enterprise productivity with customized generative AI models.

NVIDIA Omniverse

Check out the GTC Spring 2023 montage of NVIDIA Omniverse foundational technology in action.

NVIDIA Research AI Playground

Discover our most recent AI research and the new capabilities deep learning brings to visual and audio applications. Explore the latest innovations and see how you can bring them into your own work.


NVIDIA Developer Program

Join our free Developer Program to access the 600+ SDKs, AI models, community forums, tech blogs, and technical resources that can accelerate your work and advance your skills.

NVIDIA Deep Learning Institute

Upgrade your technical expertise, develop practical skills, and earn certificates showing subject matter competency with self-paced courses and instructor-led, hands-on workshops.

NVIDIA Inception for Startups

Explore the program that provides cutting-edge startups around the world with critical access to go-to-market support, technical expertise, training, and funding opportunities.

Meet Inception Startups Working in Computer Vision

NVIDIA Inception is helping over 14,000 startups worldwide faster, including many working in the field of computer vision. Explore some of our members.

Like No Place You’ve Ever Worked

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