Comments (1)
Hello I got this working I ran
python render.py -m ./output/id1
during inference on my 4gb i sawRendering progress: 100%|██████████████████████████████████████████████████████████| 2254/2254 [49:41<00:00, 1.32s/it] Rendering progress: 100%|██████████████████████████████████████████████████████████████| 50/50 [01:33<00:00, 1.87s/it]
Could you explain what is happening during the above part, due to the duration it took I'm wondering if that's a step that does not need to be repeated?
Also I have the modified dataset downloaded and was wondering if I can do cross identity reenactment with it or if I need to do some training on those persons first?
Firstly, thank you for running the code to understand our work!
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It will render the complete training and testing data separately, concluding the entire rendering process.
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You can utilize other characters within our modified data to drive the id1 avatar without the need for retraining. For instance, if you wish to reenactment id1 using id2, simply replace the facial expressions and c2w parameters in the
dataset_reader.py
with those of id2.
If you need those code, feel free to get in touch with me via email. I'd be more than happy to assist you.
from gaussian-head.
Related Issues (20)
- about render HOT 1
- About train.py HOT 3
- How to reenact? HOT 1
- train.py?
- when render loading training cameras, automatic quit HOT 1
- CUDA_HOME environment variable is not set. HOT 3
- ValueError: invalid literal for int() with base 10: 'Store' HOT 1
- inference time
- About installation of submodules\simple-knn and depth-diff-gaussian-rasterization
- head dataset
- Training Code HOT 4
- Why can't I render different head posture orientations? HOT 2
- time
- What's the specific setting for dividing the trainset and the testset? HOT 4
- More pre-trained model HOT 1
- Is this a monocular dataset
- License?
- Training Time
- Some questions about the code. HOT 1
- How to use pre-trained models?
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