Hi, thanks for developing a fascinating algorithm!
I have just attempted running it on an Nvidia RTX 4070 with 12 GB VRAM, and get a CUDA out of memory error when running bash script/fit_people_30s.sh (it attempts to allocate ~13GB). I now wonder if there are any simple ways of reducing the VRAM usage during the fitting of the model — for example, which parameters in profiles/people/people_30s.yaml have the largest effect on memory?
Additionally, what would be the preferred way of generating a textured mesh from the learned avatar?
Hi, thanks for developing a fascinating algorithm!
I have just attempted running it on an Nvidia RTX 4070 with 12 GB VRAM, and get a CUDA out of memory error when running
bash script/fit_people_30s.sh
(it attempts to allocate ~13GB). I now wonder if there are any simple ways of reducing the VRAM usage during the fitting of the model — for example, which parameters inprofiles/people/people_30s.yaml
have the largest effect on memory?Additionally, what would be the preferred way of generating a textured mesh from the learned avatar?
Thanks, Filip