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BetaThis project is currently in its beta version.Updates might take a while to roll out.There's still plenty of work left to do.Thanks for your patience while we keep building.
BetaThis project is currently in its beta version.Updates might take a while to roll out.There's still plenty of work left to do.Thanks for your patience while we keep building.
mHumanTrackAImHumanTrackAI

Troubleshooting

Common issues and how to resolve them, drawn from the project's known limitations.

CUDA silent CPU fallback

Symptom: on a machine with an NVIDIA GPU, the app runs slowly (~2 fps), the source shows rtm:cpu, and you may see a misleading "cublasLt missing" error.

Cause: if nvidia-cufft-cu12 is missing, the ONNX Runtime CUDA execution provider fails silently and falls back to CPU.

Fix: make sure the full CUDA 12 wheel set is installed (nvidia-cuda-runtime-cu12, cublas, cufft, cudnn, nvrtc). The installer installs these for you. The engine's cuda_setup.py puts the CUDA wheel bin directories on PATH before ONNX Runtime loads — but the wheels themselves must be present.

GPU acceleration is RTM-only

MediaPipe's Python GPU delegate is OpenGL-only and falls back to CPU on Windows. CUDA speedup requires the RTMW + onnxruntime-gpu path, not MediaPipe.

Model download blocked

Symptom: on first run, the RTMW model fails to download.

Cause: some networks (for example certain institutions) block the openmmlab CDN.

Fix: the app uses a Hugging Face mirror by default (Izymka/rtmw-dw-x-l_simcc-cocktail14). If that is also blocked, download the model files manually and place them in engine/models/rtm/.

Anti-cheat blocks input

Symptom: input works on the desktop but a competitive game ignores it.

Cause: many games reject SendInput. A ViGEm virtual Xbox 360 controller is accepted far more widely, but kernel-level anti-cheat can still reject synthetic input devices.

Fix: prefer Gaming Mode (virtual controller) over desktop input injection for games. Even then, some kernel-level anti-cheat systems will reject synthetic devices — this is an external hard limit that no input tool can fully work around.

TensorRT version pinning

Symptom: TensorRT acceleration silently fails to engage.

Cause: ONNX Runtime 1.26 links nvinfer_10.dll (TensorRT 10). The latest tensorrt-cu12 package is v11, which silently fails.

Fix: pin the TensorRT package below v11:

powershell
pip install "tensorrt-cu12<11"

With the correct version, RTMW reaches roughly ~90 fps (FP16) on an RTX 2060.

Single-camera depth is ambiguous

Symptom: a hand directly behind your back is only roughly placed.

Cause: one webcam gives good 2D and only rough depth; a fully occluded joint can only be predicted.

Fix: add a second camera (for example front + back). Multi-camera fusion selects whichever view actually sees each joint. See Cameras.