Hi all,

Has anyone tried running the code on Windows? Does the framework use any Linux specific libraries? Is there any information regarding the average time necessary to run the forward-pass with the pretrained models?

Best Regards -E

Hi, thanks for your great work.

I am wondering why only consider the foreground as a person.

As shown in the umbrella case, the umbrella is not a person, but also successfully segmented.

Step: 49993, Loss: 0.013911506161093712, Time: 0.13519692420959473, chain_5frames: False, expname: Balloon2_H270_DyNeRF_pretrain Step: 49994, Loss: 0.012133732438087463, Time: 0.1316220760345459, chain_5frames: False, expname: Balloon2_H270_DyNeRF_pretrain Step: 49995, Loss: 0.01327237207442522, Time: 0.1362135410308838, chain_5frames: False, expname: Balloon2_H270_DyNeRF_pretrain Step: 49996, Loss: 0.01176927424967289, Time: 0.12955403327941895, chain_5frames: False, expname: Balloon2_H270_DyNeRF_pretrain Step: 49997, Loss: 0.012316515669226646, Time: 0.13320422172546387, chain_5frames: False, expname: Balloon2_H270_DyNeRF_pretrain Step: 49998, Loss: 0.012100623920559883, Time: 0.1324453353881836, chain_5frames: False, expname: Balloon2_H270_DyNeRF_pretrain Step: 49999, Loss: 0.011780014261603355, Time: 0.13549208641052246, chain_5frames: False, expname: Balloon2_H270_DyNeRF_pretrain Traceback (most recent call last): File "run_nerf.py", line 763, in train() File "run_nerf.py", line 452, in train **render_kwargs_train) File "/DynamicNeRF/render_utils.py", line 98, in render rays, chunk, **kwargs) File "/DynamicNeRF/render_utils.py", line 26, in batchify_rays all_ret = {k: torch.cat(all_ret[k], 0) for k in all_ret} File "/DynamicNeRF/render_utils.py", line 26, in all_ret = {k: torch.cat(all_ret[k], 0) for k in all_ret} TypeError: expected Tensor as element 0 in argument 0, but got tuple

Dear gaochen, thanks for shaing your great job! I followed your lead and trained around 240k step currently, but i got no video as you mentioned in readme Every 100k iterations, you should get videos like the following examples and the filename is not the same as your's

Hi @gaochen315 ,

Thanks a lot for releasing the code for DynamicNeRF! This is one of the smoothest experiences I had so far when it comes to running a Github repo - great work!

Just one small thing I noticed is that scikit-image seems to be missing in the dependencies. When I run the motion mask generation script, an import error is raised.

Hey, i'm attempting to recreate the results from the paper, and i'm trying to download the dataset in order to test it out, and it seems the link https://www-users.cse.umn.edu/~jsyoon/dynamic_synth/ is broken

is there any other source for the data?

Fixing random seed 1 factor 2 (270, 480, 3, 12) (270, 480, 12) (270, 480, 12) (270, 480, 2, 12) (270, 480, 12) Loaded ./data/Balloon2/ 45.061882503348485 71.07477444180932 Loaded llff (12, 270, 480, 3) (60, 3, 5) [270. 480. 418.96216] ./data/Balloon2/ DEFINING BOUNDS NEAR FAR 0.0 1.0 Found ckpts ['./logs/Balloon2_H270_DyNeRF_pretrain/300000.tar', './logs/Balloon2_H270_DyNeRF_pretrain/Pretrained_S.tar'] Reloading from ./logs/Balloon2_H270_DyNeRF_pretrain/Pretrained_S.tar Traceback (most recent call last): File "run_nerf.py", line 768, in train() File "run_nerf.py", line 215, in train render_kwargs_train, render_kwargs_test, start, grad_vars, optimizer = create_nerf(args) File "/home/chenghuan/DynamicNeRF/folder/run_nerf_helpers.py", line 355, in create_nerf model_d.load_state_dict(ckpt['network_fn_d_state_dict']) KeyError: 'network_fn_d_state_dict'

Hi, thanks for your great work.

I wonder why there is no 'network_fn_d_state_dict' in ckpt.

Your model training time is slow . Can you take advantage of instant ngp-related work to increase speed?

我很疑惑为什么在我运行时ckpt中没有'network_fn_d_state_dict',您的模型训练时间很慢,我们能否利用 instant ngp 相关工作来提升您的模型的速度.期待您的回复.

Hello, I tried your great work on my own video record by phone. But I failed on the following colmap's command colmap mapper \ --database_path $DATASET_PATH/database.db \ --image_path $DATASET_PATH/images_colmap \ --output_path $DATASET_PATH/sparse \ --Mapper.num_threads 16 \ --Mapper.init_min_tri_angle 4 \ --Mapper.multiple_models 0 \ --Mapper.extract_colors 0 like this

firstly, I have some difficult to understand what does these Mapper.init_min_tri_angle Mapper.multiple_models Mapper.extract_colors mean? And what's your purpose to alter those parameters rather than using default ? Secondly, I removed the last 4 parameters, things go well and successfully got my camera.bin. I wonder how those affect the result. And I'm worry about by remove the last 4 parameters Mapper.num_threads Mapper.init_min_tri_angle Mapper.multiple_models Mapper.extract_colors, whether the extrinsic camera poses I get are still accurate or not ？

Hoping for your reply, thanks!