NVIDIA AI-Mediated Reality and Interaction Research Group

Welcome to the homepage of NVIDIA’s AI-Mediated Reality and Interaction Research Group within the larger NVIDIA Research orgnization, led by Dr. Shalini De Mello. Our mission is to re-imagine, using AI, communication between humans, and between humans and machines. Our research spans AI, vision and graphics. We are particularly interested in:

  • Digital Humans: generative models for digital humans
  • Human Understanding: embodied perception of human state and behavior
  • Core AI Technologies: neural rendering, generative AI, streaming and displays
  • New Experiences: enabled by AI (3D video conferencing, content creation, XR, LLMs, etc.)

We are looking for motivated full-time research scientists and interns! Interested graduate students please email the team lead or specific members directly.

News

February 2025 - 3 new papers accepted to CVPR 2025! We are also organizing 2 workshops and 1 tutorial, more details to come soon.

December 2024 - 1 paper published at SIGGRAPH Asia 2024 and 2 papers published at NeurIPS 2024!

August 2024 - 1 paper published in Nature, and 1 New paper accepted to ECCV 2024!

April 2024 - 4 papers accepted to CVPR 2024 and 1 paper accepted to ICLR 2024!

Members

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Shalini De Mello

Team Leader

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Amrita Mazumdar

Streaming, Compression, 3D Representations

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Jonghyun Kim

AI display research, AR/VR glasses design, Holographic near-eye displays

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Koki Nagano

Digital Humans, 3D Generative Models, Media Forensics

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Michael Stengel

Computer Graphics, Telepresence

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Omer Shapira

XR, Graphics, Human-Computer Interaction

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Seonwook Park

Computer Vision, Perceiving Humans

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Tianye Li

Digital Humans, Computer Vision, Graphics

Publications

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BLADE: Single-view Body Mesh Learning through Accurate Depth Estimation

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025

Single-image human mesh recovery is a challenging task due to the ill-posed nature of simultaneous body shape, pose, and camera estimation. Existing estimators work well on images taken from afar, but they break down as the person moves close to the …
Project arXiv PDF Video

Coherent 3D Portrait Video Reconstruction via Triplane Fusion

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025

Recent breakthroughs in single-image 3D portrait reconstruction have enabled telepresence systems to stream 3D portrait videos from a single camera in real-time, potentially democratizing telepresence. However, per-frame 3D reconstruction exhibits …
Project arXiv PDF

SimAvatar: Simulation-Ready Clothed Gaussian Avatars from Text

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025

We introduce SimAvatar, a framework designed to generate simulation-ready clothed 3D human avatars from a text prompt. Current text-driven human avatar generation methods either model hair, clothing, and the human body using a unified geometry or …
Project arXiv PDF Video

CosAE: Learnable Fourier Series for Image Restoration

Advances in Neural Information Processing Systems (NeurIPS), 2024

In this paper, we introduce Cosine Autoencoder (CosAE), a novel, generic Autoencoder that seamlessly leverages the classic Fourier series with a feed-forward neural network. CosAE represents an input image as a series of 2D Cosine time series, each …
Project PDF Video

Large Étendue 3D Holographic Display with Content-adpative Dynamic Fourier Modulation

SIGGRAPH ASIA (Conference Track), 2024

Emerging holographic display technology offers unique capabilities for next-generation virtual reality systems. Current holographic near-eye displays, however, only support a small étendue, which results in a direct tradeoff between achievable field …
Project PDF Video

QUEEN: QUantized Efficient ENcoding for Streaming Free-viewpoint Videos

Advances in Neural Information Processing Systems (NeurIPS), 2024

Online free-viewpoint video (FVV) streaming is a challenging problem, which is relatively under-explored. It requires incremental on-the-fly updates to a volumetric representation, fast training and rendering to satisfy real-time constraints and a …
Project arXiv PDF Blog

Avatar Fingerprinting for Authorized Use of Synthetic Talking-Head Videos

European Conference on Computer Vision (ECCV), 2024

Modern generators render talking-head videos with impressive photorealism, ushering in new user experiences such as videoconferencing under constrained bandwidth budgets. Their safe adoption, however, requires a mechanism to verify if the rendered …
Project arXiv PDF Video

Dream-in-4D: A Unified Approach for Text- and Image-guided 4D Scene Generation

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024

Large-scale diffusion generative models are greatly simplifying image, video and 3D asset creation from user-provided text prompts and images. However, the challenging problem of text-to-4D dynamic 3D scene generation with diffusion guidance remains …
Project arXiv Github

GAvatar: Animatable 3D Gaussian Avatars with Implicit Mesh Learning

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024
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Gaussian splatting has emerged as a powerful 3D representation that harnesses the advantages of both explicit (mesh) and implicit (NeRF) 3D representations. In this paper, we seek to leverage Gaussian splatting to generate realistic animatable …
Project arXiv

RegionGPT: Towards Region Understanding Vision Language Model

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024

Vision language models (VLMs) have experienced rapid advancements through the integration of large language models (LLMs) with image-text pairs, yet they struggle with detailed regional visual understanding due to limited spatial awareness of the …
Project arXiv
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