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How to Fix Exaggerated VRM Lip Sync with Quantiles and Saturation

A practical calibration method for VRM mouth opening: measure representative audio, track saturation, and check for expression overlap.
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If a VRM avatar’s mouth opens too far during speech, check both the audio-to-mouth mapping and any other active mouth expression. A mapping that hits its maximum on many frames loses useful variation; an expression such as happy can also combine with the procedural aa expression and exaggerate the result. Tune against representative audio, measure how often the output reaches its cap, then inspect the rendered avatar.

What VRM controls—and what it leaves to the app

VRM 1.0 standardizes facial expressions, including the optional procedural lip-sync presets aa, ih, ou, ee, and oh. Expression weights range from 0 to 1, and implementations should clamp values outside that range. The specification does not mandate how an application analyzes audio or chooses thresholds. VRM 1.0 expression specification

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Expression combinations matter: the specification warns that applying happy and aa together can make the mouth open too far. The overrideMouth property lets an application control procedural lip-sync weights while another expression is active. Whether and how an app exposes that behavior is implementation-specific. VRM 1.0 expression specification

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VRM’s feature overview describes standardized a-i-u-e-o facial operations and generating lip sync from audio, but it does not set an RMS threshold, saturation target, or quantile-based calibration method. VRM feature overview

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Calibrate the mapping with quantiles and saturation

For an amplitude-driven mapping, a baseline can represent a quiet or noise floor, while a reference level determines what input reaches the chosen mouth-opening ceiling. Quantiles help characterize the actual signal instead of tuning to one average or a single loud moment: the median describes the middle frame level, while lower and upper quantiles show quieter and louder portions. These are descriptions of a particular audio distribution, not universal VRM thresholds.

  1. Use representative speech. Collect input from the voices, microphone conditions, and speaking styles the avatar will encounter. One clip or peak cannot establish a reliable mapping for all of them.
  2. Inspect the level distribution. Look at the median and relevant lower and upper quantiles of the input levels. Use the quieter portion to assess whether speech remains visible, and the louder portion to decide where the mapping should approach its ceiling.
  3. Set a baseline and reference from those measurements. Keep the baseline near the unwanted quiet-level floor and choose a reference that maps useful speech levels into a controlled expression range. Do not treat a published value from another setup as a default.
  4. Measure output saturation. Count the frames whose mapped expression reaches its maximum and calculate the fraction of all frames they represent. A high ceiling-hit fraction means the mapping spends much of its time unable to express additional level variation.
  5. Restore headroom, then check quiet speech. If saturation is frequent, reduce mapping gain or revise the baseline or reference. Recheck quieter passages to ensure the mouth still moves, then judge the motion visually in the avatar.
  6. Check expression overlap. Inspect whether an emotion or other mouth-opening expression is active at the same time as procedural lip sync. Adjust the app’s expression handling, including any available mouth override behavior, if the combination causes excessive opening.

One published TTS tuning example by orca_forge reports median frame RMS of 0.214, 25th-percentile frame RMS of 0.024, and 90th-percentile frame RMS of 0.403. In that article’s on-device example, a volume baseline of 0.15 produced 58.5% saturated frames. The page’s publication year is not established here; these figures describe that example, not a recommended saturation rate or general values for other voices and pipelines. orca_forge’s RMS-based VRM tuning article

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Know when loudness-based lip sync is not enough

RMS measures signal energy, not the phoneme being spoken. It can make mouth opening follow loudness, but it cannot reliably represent some lip closures, including bilabial sounds. If articulation accuracy matters, use a phoneme- or viseme-aware method rather than expecting an RMS envelope to reproduce speech gestures. orca_forge’s RMS-based VRM tuning article

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The three-vrm-lip-sync project documents runtime controls for gain, smoothness, minimum and maximum volume, and viseme gain. Those controls are specific to that project; its README is not a comparative benchmark. three-vrm-lip-sync README

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Confirm which controls your application actually provides

Do not assume that a mouth-motion setting in one VRM application is an audio lip-sync control in another. VRMViewMeister 2.18.0 documents opening and closing speeds for auxiliary mouth motion, and explicitly distinguishes that feature from making the model move its mouth in response to speech. Verify the application and version before following UI instructions or relying on a particular control. VRMViewMeister documentation

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