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Re-illuminate people in video to match target lighting from a 360 HDRI environment map.
The Video Relighting model re-illuminates a person in a video to match the target lighting provided by a 360 High Dynamic Range Image (HDRI).
Video Relighting is available under NVIDIA AI for Media— a developer platform for deploying AI features that enhance audio, video, and creating new experiences in real-time audio-video communication. NVIDIA AI for Media's state-of-the-art models create high-quality AI effects using standard microphones and cameras without additional special equipment.
NVIDIA AI for Media is exclusively part of NVIDIA AI Enterprise for production workflows — an extensive library of full-stack software, including AI solution workflows, frameworks, pre-trained models, and infrastructure optimization.
This model is ready for commercial/non-commercial use.
Refer to NVIDIA SOFTWARE LICENSE AGREEMENT and Product-Specific Terms for NVIDIA AI Products. Use of the models is governed by the NVIDIA Open Model License.
Global
Virtual Production: Match lighting between real subjects and virtual environments for seamless compositing.
Video Conferencing: Adjust participant lighting to match virtual backgrounds or improve appearance.
Content Creation: Enhance lighting consistency when combining footage from different sources.
NGC - 01/06/2026 via https://catalog.ngc.nvidia.com/orgs/nvidia/teams/maxine/models/nvvfxrelighting
Architecture Type: Convolution Neural Network (CNN), Generative Adversarial Networks (GANs) Network Architecture: ResNet, UNET
Input Type(s): Image
Input Format(s): Red, Green, Blue (RGB)
Input Parameters: Two-Dimensional (2D)
Other Properties Related to Input: Input image 360p to 4k, Input HDR Image with extension .hdr or .exr. F16/F32 data type.
Output Type(s): Image
Output Format: Red, Green, Blue (RGB)
Output Parameters: Two-Dimensional (2D)
Other Properties Related to Output: Output resolution is the same as input.
Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
Runtime Engine(s):
Supported Hardware Microarchitecture Compatibility:
[Preferred/Supported] Operating System(s):
The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
Link:
Data Collection Method by dataset
Hybrid: Human and Synthetic
Labeling Method by dataset
Hybrid: Automated and Synthetic
Properties (Quantity, Dataset Descriptions, Sensor(s)): Synthetic dataset contains 600k training samples of image, HDRIs and intermediate ground truth under paired lighting conditions. Real dataset contains around 40k images and 50k videos of people in different lighting environments. HDR dataset contains over 8000 HDR images.
Data Modality
Image Training Data Size
Link:
Data Collection Method by dataset
Human
Labeling Method by dataset
Automated
Properties (Quantity, Dataset Descriptions, Sensor(s)):
Around 200 videos with various lengths of a single person in front of the camera conducting a video conference or broadcasting. The dataset varies in terms of quality, lighting, head pose, gaze angles and other diversity factors such as race, eye color, and gender.
Link:
Data Collection Method by dataset
Synthetic
Labeling Method by dataset
Synthetic
Properties (Quantity, Dataset Descriptions, Sensor(s)):
50 sets of image-albedo pairs rendered using TripleGanger assets and 600 Polyhaven HDRI images using Omniverse Kit.
Engine: TensorRT
Test Hardware:
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Please make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included.
For more detailed information on ethical considerations for this model, please see the Model Card++ Bias, Explainability, Safety & Security, and Privacy Subcards.
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