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Field	Response
Participation considerations from adversely impacted groups protected classes in model design and testing:	None
Measures taken to mitigate against unwanted bias:	None

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NVIDIA Audio2Face-3D is a microservice for animating a 3D character's facial expressions to match any audio track, whether for a game, film, or real-time digital assistant. This repository contains the Audio2Face TensorRT engines, including regression models (claire v2.3.1, james v2.3.1, mark v2.3) and the diffusion model (multi v3.2), optimized for inference across 10 supported NVIDIA GPU platforms.

Model Overview

Description:

Audio2Face-3D generates 3D facial animations from audio inputs, for use in applications such as video conferencing, virtual reality, and digital content creation.\nThis model is ready for commercial/non-commercial use.

License/Terms of Use

Use of this model is governed by the NVIDIA Open Model License

Deployment Geography: Global

Use Case:

Audio2Face-3D is designed for developers and researchers working on audio-driven animation and emotion detection applications, such as virtual assistants, chatbots, and affective computing systems.

Release Date:

Hugging Face: 08/27/2025 via
\nhttps://huggingface.co/nvidia/Audio2Face-3D-v2.3-Mark
\nhttps://huggingface.co/nvidia/Audio2Face-3D-v2.3.1-Claire
\nhttps://huggingface.co/nvidia/Audio2Face-3D-v2.3.1-James
\nhttps://huggingface.co/nvidia/Audio2Face-3D-v3.0

References(s):

NVIDIA, Audio2Face-3D: Audio-driven Realistic Facial Animation For Digital Avatars, 2025.\nhttps://arxiv.org/abs/2508.16401\n

\n
\n

Model Architecture:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

	Audio2Face-3D-v2.3	Audio2Face-3D-v3.0
Architecture Type	Transformer, CNN	Transformer, Diffusion
Network Architecture	Wav2vec2.0	Hubert
Number of Model Parameters	3.98x10^7	1.80x10^8

Input:

Input Type(s): Audio
\nInput Format: Array of float
\nInput Parameters: One-Dimensional (1D)
\nOther Properties Related to Input: All audio is resampled to 16KHz

Output:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

	Audio2Face-3D-v2.3	Audio2Face-3D-v3.0
Output Type(s)	Facial pose	Facial motion
Output Format	Array of float	Array of float
Output Parameters	One-Dimensional (1D)	Two-Dimensional (2D)
Other Properties Related to Output	Facial pose on skin, tongue, jaw, and eyeballs	Facial motion on skin, tongue, jaw, and eyeballs

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.

Software Integration:

Runtime Engine(s):

Audio2Face-SDK

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Blackwell
NVIDIA Hopper
NVIDIA Lovelace
NVIDIA Pascal
NVIDIA Turing

Preferred/Supported Operating System(s):

Linux
Windows

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.
\nThis AI model can be embedded as an Application Programming Interface (API) call into the software environment described above.

Model Version(s):

Audio2Face-3D-v2.3
\nAudio2Face-3D-v3.0

Training, Testing, and Evaluation Datasets:

Training Dataset:

** Data Modality

Audio
3D facial motion

** Audio Training Data Size

Less than 10,000 Hours

** Data Collection Method by dataset

Human - 3D facial motion data and audio

** Labeling Method by dataset

Human - Commercial capture solution and internal labeling

Properties (Quantity, Dataset Descriptions, Sensor(s)): Audio and 3D facial motion from multiple speech sequences

Testing Dataset:

Data Collection Method by dataset:

Human - 3D facial motion data and audio

Labeling Method by dataset:

Human - Commercial capture solution and internal labeling

Properties (Quantity, Dataset Descriptions, Sensor(s)): Audio and 3D facial motion from multiple speech sequences

Evaluation Dataset:

Data Collection Method by dataset:

Human - 3D facial motion data and audio

Labeling Method by dataset:

Human - Commercial capture solution and internal labeling

Properties (Quantity, Dataset Descriptions, Sensor(s)): Audio and 3D facial motion from multiple speech sequences

Inference:

Acceleration Engine: TensorRT
\nTest Hardware:

T4, T10, A10, A40, L4, L40S, A100
RTX 6000ADA, A6000, Pro 6000 Blackwell
RTX 3080, 3090, 4080, 4090, 5090

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

For more detailed information on ethical considerations for this model, please see the Model Card++ Bias, Explainability, Safety & Security, and Privacy Subcards.\nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here

","resourceVersions","resourceVersionsTotal","hasMoreVersions","versionMetadata","versionNotFound","isLoggedIn","latestVersionPath","/orgs/nim/nvidia/models/audio2face_3d_model/-","version","-","imageReference","cliDownloadCommand","ngc registry model download-version nim/nvidia/audio2face_3d_model:multi_v3.2_b200_fp16_bs144_v6","helmFetchCommand","viewAllVersionsPath","/orgs/nim/nvidia/models/audio2face_3d_model/-/version-history","accessCheckResult",{"_104":105,"_106":20},"hasRelatedCollections","context",{"_100":101,"_102":103},1783983075209,[],[],[93,94,95,96,97,98],{"_33":47,"_35":57,"_37":49,"_39":50,"_41":58,"_43":44},{"_33":47,"_35":54,"_37":38,"_39":55,"_41":56,"_43":44},{"_33":47,"_35":52,"_37":38,"_39":55,"_41":99,"_43":44},{"_33":47,"_35":48,"_37":49,"_39":50,"_41":51,"_43":44},{"_33":45,"_35":46,"_37":38,"_39":40,"_41":42,"_43":44},{"_33":34,"_35":36,"_37":38,"_39":40,"_41":42,"_43":44},48,"orgName","nim","teamName","nvidia","reason","login_required","allowed","id","nim/nvidia/audio2face_3d_model","key","nim/nvidia/models/audio2face_3d_model","resourceType","models","resourceId","audio2face_3d_model","shortDescription","NVIDIA Audio2Face is a microservice for animating 3D character's facial characteristics to match any audio track, whether for a game, film, or real-time digital assistant.","description","NVIDIA Audio2Face-3D is a microservice for animating a 3D character's facial expressions to match any audio track, whether for a game, film, or real-time digital assistant. This repository contains the Audio2Face TensorRT engines, including regression models (claire v2.3.1, james v2.3.1, mark v2.3) and the diffusion model (multi v3.2), optimized for inference across 10 supported NVIDIA GPU platforms.\n\n# Model Overview\n\n### Description:\nAudio2Face-3D generates 3D facial animations from audio inputs, for use in applications such as video conferencing, virtual reality, and digital content creation.\nThis model is ready for commercial/non-commercial use.
\n\n### License/Terms of Use\n\nUse of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/)
\n\n### Deployment Geography: Global
\n\n### Use Case:
\nAudio2Face-3D is designed for developers and researchers working on audio-driven animation and emotion detection applications, such as virtual assistants, chatbots, and affective computing systems.
\n\n### Release Date:
\nHugging Face: 08/27/2025 via \\\nhttps://huggingface.co/nvidia/Audio2Face-3D-v2.3-Mark \\\nhttps://huggingface.co/nvidia/Audio2Face-3D-v2.3.1-Claire \\\nhttps://huggingface.co/nvidia/Audio2Face-3D-v2.3.1-James \\\nhttps://huggingface.co/nvidia/Audio2Face-3D-v3.0
\n\n## References(s):\n```\nNVIDIA, Audio2Face-3D: Audio-driven Realistic Facial Animation For Digital Avatars, 2025.\nhttps://arxiv.org/abs/2508.16401\n```\n
\n\n## Model Architecture:\n\n| | Audio2Face-3D-v2.3 | Audio2Face-3D-v3.0 |\n|---|---|---|\n| **Architecture Type** | Transformer, CNN | Transformer, Diffusion |\n| **Network Architecture** | Wav2vec2.0 | Hubert |\n| **Number of Model Parameters** | 3.98x10^7 | 1.80x10^8 |\n\n## Input:
\n**Input Type(s):** Audio
\n**Input Format:** Array of float
\n**Input Parameters:** One-Dimensional (1D)
\n**Other Properties Related to Input:** All audio is resampled to 16KHz\n\n## Output:
\n\n| | Audio2Face-3D-v2.3 | Audio2Face-3D-v3.0 |\n|---|---|---|\n| **Output Type(s)** | Facial pose | Facial motion |\n| **Output Format** | Array of float | Array of float |\n| **Output Parameters** | One-Dimensional (1D) | Two-Dimensional (2D) |\n| **Other Properties Related to Output** | Facial pose on skin, tongue, jaw, and eyeballs | Facial motion on skin, tongue, jaw, and eyeballs |\n\nOur 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.
\n\n## Software Integration:\n**Runtime Engine(s):** \n* Audio2Face-SDK
\n\n**Supported Hardware Microarchitecture Compatibility:**
\n* NVIDIA Ampere
\n* NVIDIA Blackwell
\n* NVIDIA Hopper
\n* NVIDIA Lovelace
\n* NVIDIA Pascal
\n* NVIDIA Turing
\n\n**Preferred/Supported Operating System(s):**\n* Linux
\n* Windows
\n\nThe 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.
\nThis AI model can be embedded as an Application Programming Interface (API) call into the software environment described above.
\n\n## Model Version(s):\nAudio2Face-3D-v2.3
\nAudio2Face-3D-v3.0
\n\n## Training, Testing, and Evaluation Datasets:\n\n## Training Dataset:\n\n** Data Modality
\n* Audio
\n* 3D facial motion
\n\n** Audio Training Data Size
\n* Less than 10,000 Hours
\n\n** Data Collection Method by dataset
\n* Human - 3D facial motion data and audio
\n\n** Labeling Method by dataset
\n* Human - Commercial capture solution and internal labeling
\n\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** Audio and 3D facial motion from multiple speech sequences
\n\n### Testing Dataset:\n\nData Collection Method by dataset:
\n* Human - 3D facial motion data and audio
\n\nLabeling Method by dataset:
\n* Human - Commercial capture solution and internal labeling
\n\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** Audio and 3D facial motion from multiple speech sequences
\n\n\n### Evaluation Dataset:\n\nData Collection Method by dataset:
\n* Human - 3D facial motion data and audio
\n\nLabeling Method by dataset:
\n* Human - Commercial capture solution and internal labeling
\n\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** Audio and 3D facial motion from multiple speech sequences
\n\n\n# Inference:\n**Acceleration Engine:** TensorRT
\n**Test Hardware:**
\n* T4, T10, A10, A40, L4, L40S, A100
\n* RTX 6000ADA, A6000, Pro 6000 Blackwell
\n* RTX 3080, 3090, 4080, 4090, 5090
\n\n## Ethical Considerations:\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.\n\nFor more detailed information on ethical considerations for this model, please see the Model Card++ Bias, Explainability, Safety & Security, and Privacy Subcards. \nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/)","displayName","Audio2Face-3D Model","updatedDate",["D",1772847092534],"publisher","NVIDIA","latestVersion","multi_v3.2_b200_fp16_bs144_v6","versions","collectionCounts","imageUrl","/api/image/nim/nvidia/models/audio2face_3d_model","rawImageUrl","https://assets.ngc.nvidia.com/products/api-catalog/images/audio2face-3d.jpg","canGuestDownload","latestVersionSizeInBytes",732956700,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","Field | Response\n:---------------------------------------------------------------------------------------------------|:---------------\nParticipation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None\nMeasures taken to mitigate against unwanted bias: | None","explainability","Field | Response\n:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nIntended Task/Domain: | Customer Service, Media & Entertainment\nModel Type: | Transformer, CNN\nIntended Users: | Interactive avatar developers, Digital content creators\nOutput: | Facial pose\nDescribe how the model works: | Audio input is encoded and concatenated with emotion label, then passed into CNN-based pose decoder to output facial pose.\nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable\nTechnical Limitations & Mitigation: | This model may not work well with poor audio input.\nVerified to have met prescribed NVIDIA quality standards: | Yes\nPerformance Metrics: | Lipsync accuracy, Latency, Throughput\nPotential Known Risks: | This model may generate inaccurate lip poses given low-quality audio input.\nLicensing: | Use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/)","privacy","Field | Response\n:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------\nGeneratable or reverse engineerable personal data? | No\nPersonal data used to create this model? | Yes\nWas consent obtained for any personal data used? | Yes\nHow often is dataset reviewed? | Before Release\nIs a mechanism in place to honor data subject right of access or deletion of personal data? | Yes\nIf personal data was collected for the development of the model, was it collected directly by NVIDIA? | Yes\nIf personal data was collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Yes\nIf personal data was collected for the development of this AI model, was it minimized to only what was required? | Yes\nIs there provenance for all datasets used in training? | Yes\nDoes data labeling (annotation, metadata) comply with privacy laws? | Yes\nIs data compliant with data subject requests for data correction or removal, if such a request was made? | Yes\nApplicable Privacy Policy | https://www.nvidia.com/en-us/about-nvidia/privacy-policy/","safetyAndSecurity","Field | Response\n:---------------------------------------------------|:----------------------------------\nModel Application Field(s): | Customer Service, Media & Entertainment\nDescribe the life critical impact (if present). | Not Applicable \nUse Case Restrictions: | Abide by [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/)\nModel and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. 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We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438],[276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292],{"_293":400,"_119":400,"_117":295,"_266":401},{"_293":395,"_119":395,"_117":295,"_266":396},{"_293":391,"_119":391,"_117":295,"_266":392},{"_293":386,"_119":386,"_117":295,"_266":387},{"_293":381,"_119":381,"_117":295,"_266":382},{"_293":369,"_119":369,"_117":295,"_266":370},{"_293":357,"_119":357,"_117":295,"_266":358},{"_293":352,"_119":352,"_117":295,"_266":353},{"_293":341,"_119":341,"_117":295,"_266":342},{"_293":336,"_119":336,"_117":295,"_266":337},{"_293":331,"_119":331,"_117":295,"_266":332},{"_293":326,"_119":326,"_117":295,"_266":327},{"_293":321,"_119":321,"_117":295,"_266":322},{"_293":316,"_119":316,"_117":295,"_266":317},{"_293":311,"_119":311,"_117":295,"_266":312},{"_293":304,"_119":304,"_117":295,"_266":305},{"_293":294,"_119":294,"_117":295,"_266":296},"hostName","amazon-adsystem.com","",[297,298],{"_260":303,"_300":294,"_301":20,"_117":302},{"_260":299,"_300":294,"_301":20,"_117":302},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[306,307],{"_260":310,"_300":304,"_301":20,"_117":309},{"_260":308,"_300":304,"_301":20,"_117":309},"IDE","This domain is owned by Doubleclick (Google). 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It typically acts as a third party host where website owners have placed one of its content sharing buttons in their pages, although its content and services can be embedded in other ways. Although such buttons add functionality to the website they are on, cookies are set regardless of whether or not the visitor has an active Linkedin profile, or agreed to their terms and conditions. For this reason it is classified as a primarily tracking/targeting domain.","twitter.com",[359,360,361,362],{"_260":368,"_300":357,"_301":20,"_117":364},{"_260":366,"_300":357,"_301":20,"_117":367},{"_260":365,"_300":357,"_301":20,"_117":364},{"_260":363,"_300":357,"_301":20,"_117":364},"personalization_id","This domain is owned by Twitter. The main business activity is: Social Networking Services. 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It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","AMP_MKTG_91bf70adfe","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547],[492,493,494,495,496],{"_293":386,"_119":386,"_117":295,"_266":518},{"_293":352,"_119":352,"_117":295,"_266":513},{"_293":506,"_119":506,"_117":295,"_266":507},{"_293":502,"_119":502,"_117":295,"_266":503},{"_293":497,"_119":497,"_117":295,"_266":498},"nvcrm.my.site.com",[499],{"_260":500,"_300":497,"_301":20,"_117":501},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[504],{"_260":505,"_300":502,"_301":20,"_117":335},"edge","github.com",[508,509],{"_260":512,"_300":506,"_301":20,"_117":511},{"_260":510,"_300":506,"_301":20,"_117":511},"logged_in","This domain is owned by Github. 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