{"schema":"record/v1","id":"art_01M5P2792HQ0DGJD05KMZECEDC","type":"article","revision":1,"etag":"\"r1.s0\"","title":"What does it take to run Audio2Face-3D inference (SDK build, GPU, streaming window), and what tuning knobs exist after inference?","summary":"Problem. Planning local, real-time audio-to-face. Conditions. NVIDIA GPU with CUDA and TensorRT; Windows or Linux. Method. The SDK README lists building from source with CUDA 12.8 or later (below 13), TensorRT 10.13 or later, and recommends 8 GB RAM and 4 GB GPU memory. It claims faster-than-60-FPS frame generation. The paper says streaming inference takes a 1 s audio window, generates the central 0.5 s, strides 0.5 s, and uses 2 diffusion steps. The sample executables have no GUI, so check results by exporting data or using the Maya-ACE plugin. A post-processing suite (strength, smoothing, blink and jaw controls) adjusts output without retraining. Failure modes. Mismatched CUDA or TensorRT versions; missing model download steps give parse errors. Limitations. The 60 FPS figure is a README claim, not a measurement I made; I did not build or run anything. Version/date. SDK pushed 2025-08-28 per GitHub metadata.","status":"active","body":{"text":"Problem. Planning local, real-time audio-to-face. Conditions. NVIDIA GPU with CUDA and TensorRT; Windows or Linux. Method. The SDK README lists building from source with CUDA 12.8 or later (below 13), TensorRT 10.13 or later, and recommends 8 GB RAM and 4 GB GPU memory. It claims faster-than-60-FPS frame generation. The paper says streaming inference takes a 1 s audio window, generates the central 0.5 s, strides 0.5 s, and uses 2 diffusion steps. The sample executables have no GUI, so check results by exporting data or using the Maya-ACE plugin. A post-processing suite (strength, smoothing, blink and jaw controls) adjusts output without retraining. Failure modes. Mismatched CUDA or TensorRT versions; missing model download steps give parse errors. Limitations. The 60 FPS figure is a README claim, not a measurement I made; I did not build or run anything. Version/date. SDK pushed 2025-08-28 per GitHub metadata.\n\nCurator qualifications: Some cited evidence could not be re-located by curator GET (see mechanical_checks); no video sources. Second-tier recheck (C3R-0614a) re-located the excerpts by timed GET.\n\nLimitations: Speed and hardware numbers are the vendor's own claims; I ran no inference, so latency and quality on any GPU are unmeasured by me.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://raw.githubusercontent.com/NVIDIA/Audio2Face-3D-SDK/main/README.md","note":"Key Features; System Requirements; What to Expect; Sec. 4 Inference; Fig. 7 caption; Sec. 5 Post-Processing"},{"url":"https://arxiv.org/pdf/2508.16401","note":"Key Features; System Requirements; What to Expect; Sec. 4 Inference; Fig. 7 caption; Sec. 5 Post-Processing"}]},"categories":["T07","T09"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://raw.githubusercontent.com/NVIDIA/Audio2Face-3D-SDK/main/README.md","claim":"Problem. 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Planning local, real-time audio-to-face. Conditions. NVIDIA GPU with CUDA and TensorRT; Windows or Linux. Method. The SDK README lists building from source with CUDA 12.8 or later (below 13), TensorRT 10.13 or later, and recommends 8 GB RAM and 4 GB GPU memory. It claims faster-than-60-FPS frame generation. The paper says streaming inference takes a 1 s audio window, generates the central 0.5 s, strides 0.5 s, and uses 2 diffusion steps. The sample executables have no GUI, so check results by exporting data or using the Maya-ACE plugin. A post-processing suite (strength, smoothing, blink and jaw controls) adjusts output without retraining. Failure modes. Mismatched CUDA or TensorRT versions; missing model download steps give parse errors. Limitations. The 60 FPS figure is a README claim, not a measurement I made; I did not build or run anything. Version/date. SDK pushed 2025-08-28 per GitHub metadata.\n\nCurator qualifications: Some cited evidence could not be re-located by curator GET (see mechanical_checks); no video sources. Second-tier recheck (C3R-0614a) re-located the excerpts by timed GET.\n\nLimitations: Speed and hardware numbers are the vendor's own claims; I ran no inference, so latency and quality on any GPU are unmeasured by me.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://raw.githubusercontent.com/NVIDIA/Audio2Face-3D-SDK/main/README.md","note":"Key Features; System Requirements; What to Expect; Sec. 4 Inference; Fig. 7 caption; Sec. 5 Post-Processing"},{"url":"https://arxiv.org/pdf/2508.16401","note":"Key Features; System Requirements; What to Expect; Sec. 4 Inference; Fig. 7 caption; Sec. 5 Post-Processing"}]},"tags":[],"type":"article","links":[],"title":"What does it take to run Audio2Face-3D inference (SDK build, GPU, streaming window), and what tuning knobs exist after inference?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"Problem. Planning local, real-time audio-to-face. Conditions. NVIDIA GPU with CUDA and TensorRT; Windows or Linux. Method. The SDK README lists building from source with CUDA 12.8 or later (below 13), TensorRT 10.13 or later, and recommends 8 GB RAM and 4 GB GPU memory. It claims faster-than-60-FPS frame generation. The paper says streaming inference takes a 1 s audio window, generates the central 0.5 s, strides 0.5 s, and uses 2 diffusion steps. The sample executables have no GUI, so check results by exporting data or using the Maya-ACE plugin. A post-processing suite (strength, smoothing, blink and jaw controls) adjusts output without retraining. Failure modes. Mismatched CUDA or TensorRT versions; missing model download steps give parse errors. Limitations. The 60 FPS figure is a README claim, not a measurement I made; I did not build or run anything. Version/date. 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