Comments (3)
Hi, the rendering FPS depends on the GPU version and its working state. It is possible to reach 300 FPS as the code structure is optimized before it is released.
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Hi @skhu101 et al., Would you mind giving some insights/tips on how to boost FPS further or sharing some snippets? Great thx & best,
from gauhuman.
Hi, maybe one possible solution is to further optimize the number of 3D Gaussians. You can also try improving the parallel mechanism as the GPU memory is not fully used.
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Related Issues (20)
- Big pose visualization is strange HOT 2
- CUDA OUT OF MEMORY HOT 2
- transforms parameter in gaussian model deformation function HOT 1
- not enough values to unpack (expected 4, got 2) HOT 2
- Incorrect Results from SMPL Estimations with Monocular Data HOT 4
- CalledProcessError: Command '['ninja', '-v']' returned non-zero exit status 1.
- Training failed about monocap dataset HOT 3
- about new observation rendering HOT 2
- hello HOT 2
- Request for Training Code Used for InstantAvatar HOT 1
- dataset about People_SnapShot HOT 8
- How can I get the SMPL model of the custom dataset
- about mask loss HOT 1
- Generalization Performance about KL split/clone HOT 2
- Is GauHuman using unmodified GSplat Rasterizer? HOT 1
- Adaption to SMPLX HOT 3
- Some question about ZJUMOCAP-refine dataset HOT 1
- Questions about annots.npy and the Training process HOT 4
- transform of normal HOT 2
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