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facebookresearch / banmo

BANMo Building Animatable 3D Neural Models from Many Casual Videos
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BANMo

[Project page] [Paper] [Colab for NVS]

This repo provides scripts to reproduce experiments in the paper. For the latest updates on the software, please check out lab4d.

Changelog

Install

Build with conda

We provide two versions.

[A. torch1.10+cu113 (1.4x faster on V100)] ``` # clone repo git clone git@github.com:facebookresearch/banmo.git --recursive cd banmo # install conda env conda env create -f misc/banmo-cu113.yml conda activate banmo-cu113 # install pytorch3d (takes minutes), kmeans-pytorch pip install -e third_party/pytorch3d pip install -e third_party/kmeans_pytorch # install detectron2 python -m pip install detectron2 -f \ https://dl.fbaipublicfiles.com/detectron2/wheels/cu113/torch1.10/index.html ```
[B. torch1.7+cu110] ``` # clone repo git clone git@github.com:facebookresearch/banmo.git --recursive cd banmo # install conda env conda env create -f misc/banmo.yml conda activate banmo # install kmeans-pytorch pip install -e third_party/kmeans_pytorch # install detectron2 python -m pip install detectron2 -f \ https://dl.fbaipublicfiles.com/detectron2/wheels/cu110/torch1.7/index.html ```

Data

We provide two ways to obtain data. The easiest way is to download and unzip the pre-processed data as follows.

[Download pre-processed data] We provide preprocessed data for cat and human. Download the pre-processed `rgb/mask/flow/densepose` images as follows ``` # (~8G for each) bash misc/processed/download.sh cat-pikachiu bash misc/processed/download.sh human-cap ```
[Download raw videos] Download raw videos to `./raw/` folder ``` bash misc/vid/download.sh cat-pikachiu bash misc/vid/download.sh human-cap bash misc/vid/download.sh dog-tetres bash misc/vid/download.sh cat-coco ```

To use your own videos, or pre-process raw videos into banmo format, please follow the instructions here.

PoseNet weights

[expand] Download pre-trained PoseNet weights for human and quadrupeds ``` mkdir -p mesh_material/posenet && cd "$_" wget $(cat ../../misc/posenet.txt); cd ../../ ```

Demo

This example shows how to reconstruct a cat from 11 videos and a human from 10 videos. For more examples, see here.

Hardware/time for running the demo The [short schedule](./scripts/template-short.sh) takes 4 hours on 2 V100 GPUs (+SSD storage). To reach higher quality, the [full schedule](./scripts/template.sh) takes 12 hours. We provide a [script](./scripts/template-accu.sh) that use gradient accumulation to support experiments on fewer GPUs / GPU with lower memory.
Setting good hyper-parameter for videos with various length When optimizing videos with different lengths, we found it useful to scale batchsize with the number of frames. A rule of thumb is to set "num gpus" x "batch size" x "accu steps" ~= num frames. This means more video frames needs more GPU memory but the same optimization time.
Try pre-optimized models We provide [pre-optimized models](https://www.dropbox.com/sh/5ue6tpsqmt6gstw/AAB9FD6on0UZDnThr6GEde46a?dl=0) and scripts to run novel view synthesis and mesh extraction (results saved at `tmp/*all.mp4`). Also see this [Colab for NVS](https://colab.research.google.com/drive/1dQJn1vsuz0DkyRZbOA1SulkVQ0V1kMUP?usp=sharing). ``` # download pre-optimized models mkdir -p tmp && cd "$_" wget https://www.dropbox.com/s/qzwuqxp0mzdot6c/cat-pikachiu.npy wget https://www.dropbox.com/s/dnob0r8zzjbn28a/cat-pikachiu.pth wget https://www.dropbox.com/s/p74aaeusprbve1z/opts.log # flags used at opt time cd ../ seqname=cat-pikachiu # render novel views bash scripts/render_nvs.sh 0 $seqname tmp/cat-pikachiu.pth 5 0 # argv[1]: gpu id # argv[2]: sequence name # argv[3]: path to the weights # argv[4]: video id used for pose traj # argv[5]: video id used for root traj # Extract articulated meshes and render bash scripts/render_mgpu.sh 0 $seqname tmp/cat-pikachiu.pth \ "0 5" 64 # argv[1]: gpu id # argv[2]: sequence name # argv[3]: weights path # argv[4]: video id separated by space # argv[5]: resolution of running marching cubes (use 256 to get higher-res mesh) ```

1. Optimization

[cat-pikachiu] ``` seqname=cat-pikachiu # To speed up data loading, we store images as lines of pixels). # only needs to run it once per sequence and data are stored python preprocess/img2lines.py --seqname $seqname # Optimization bash scripts/template.sh 0,1 $seqname 10001 "no" "no" # argv[1]: gpu ids separated by comma # args[2]: sequence name # args[3]: port for distributed training # args[4]: use_human, pass "" for human cse, "no" for quadreped cse # args[5]: use_symm, pass "" to force x-symmetric shape # Extract articulated meshes and render bash scripts/render_mgpu.sh 0 $seqname logdir/$seqname-e120-b256-ft2/params_latest.pth \ "0 1 2 3 4 5 6 7 8 9 10" 256 # argv[1]: gpu id # argv[2]: sequence name # argv[3]: weights path # argv[4]: video id separated by space # argv[5]: resolution of running marching cubes (256 by default) ``` https://user-images.githubusercontent.com/13134872/154554031-332e2355-3303-43e3-851c-b5812699184b.mp4
[human-cap] ``` seqname=adult7 python preprocess/img2lines.py --seqname $seqname bash scripts/template.sh 0,1 $seqname 10001 "" "" bash scripts/render_mgpu.sh 0 $seqname logdir/$seqname-e120-b256-ft2/params_latest.pth \ "0 1 2 3 4 5 6 7 8 9" 256 ``` https://user-images.githubusercontent.com/13134872/154554210-3bb0a439-fe46-4ea3-a058-acecf5f8dbb5.mp4

2. Visualization tools

[Tensorboard] ``` # You may need to set up ssh tunneling to view the tensorboard monitor locally. screen -dmS "tensorboard" bash -c "tensorboard --logdir=logdir --bind_all" ```
[Root pose, rest mesh, bones] To draw root pose trajectories (+rest shape) over epochs ``` # logdir logdir=logdir/$seqname-e120-b256-init/ # first_idx, last_idx specifies what frames to be drawn python scripts/visualize/render_root.py --testdir $logdir --first_idx 0 --last_idx 120 ``` Find the output at `$logdir/mesh-cam.gif`. During optimization, the rest mesh and bones at each epoch are saved at `$logdir/*rest.obj`. https://user-images.githubusercontent.com/13134872/154553887-1871fdea-24f4-4a79-8689-86ff6af7fa52.mp4
[Correspondence/pose code] To visualize 2d-2d and 2d-3d matchings of the latest epoch weights ``` # 2d matches between frame 0 and 100 via 2d->feature matching->3d->geometric warp->2d bash scripts/render_match.sh $logdir/params_latest.pth "0 100" "--render_size 128" ``` 2d-2d matches will be saved to `tmp/match_%03d.jpg`. 2d-3d feature matches of frame 0 will be saved to `tmp/match_line_pred.obj`. 2d-3d geometric warps of frame 0 will be saved to `tmp/match_line_exp.obj`. near-plane frame 0 will be saved to `tmp/match_plane.obj`. Pose code visualization will be saved at `tmp/code.mp4`. https://user-images.githubusercontent.com/13134872/154553652-c93834db-cce2-4158-a30a-21680ab46a63.mp4
[Render novel views] Render novel views at the canonical camera coordinate ``` bash scripts/render_nvs.sh 0 $seqname logdir/$seqname-e120-b256-ft2/params_latest.pth 5 0 # argv[1]: gpu id # argv[2]: sequence name # argv[3]: path to the weights # argv[4]: video id used for pose traj # argv[5]: video id used for root traj ``` Results will be saved at `logdir/$seqname-e120-b256-ft2/nvs*.mp4`. https://user-images.githubusercontent.com/13134872/155441493-38bf7a02-a6ee-4f2f-9dc5-0cf98a4c7c45.mp4
[Render canonical view over iterations] Render depth and color of the canonical view over optimization iterations ``` bash scripts/visualize/nvs_iter.sh 0 logdir/$seqname-e120-b256-init/ # argv[1]: gpu id # argv[2]: path to the logdir ``` Results will be saved at `logdir/$seqname-e120-b256-init/vis-iter*.mp4`. https://user-images.githubusercontent.com/13134872/162283256-49f9de87-0bce-4f7f-9376-651a170e8879.mp4 https://user-images.githubusercontent.com/13134872/162283257-7636462b-c698-4411-9084-57f7a0bb89e8.mp4

Common install issues

[expand] * Q: pyrender reports `ImportError: Library "GLU" not found.` * install `sudo apt install freeglut3-dev` * Q: ffmpeg reports `libopenh264.so.5` not fund * resinstall ffmpeg in conda `conda install -c conda-forge ffmpeg`

Note on arguments

[expand] - use `--use_human` for human reconstruction, otherwise it assumes quadruped animals - use `--full_mesh` to disable visibility check at mesh extraction time - use `--noce_color` at mesh extraction time to assign radiance instead canonical mapping as vertex colors. - use `--queryfw` at mesh extraction time to extract forward articulated meshes, which only needs to run marching cubes once. - use `--use_cc` maintains the largest connected component for rest mesh in order to set the object bounds and near-far plane (by default turned on). Turn it off with `--nouse_cc` for disconnected objects such as hands. - use `--debug` to print out the rough time each component takes.

Acknowledgement

[expand] Volume rendering code is borrowed from [Nerf_pl](https://github.com/kwea123/nerf_pl). Flow estimation code is adapted from [VCN-robust](https://github.com/gengshan-y/rigidmask). Other external repos: - [Detectron2](https://github.com/facebookresearch/detectron2) (modified) - [SoftRas](https://github.com/ShichenLiu/SoftRas) (modified, for synthetic data generation) - [Chamfer3D](https://github.com/ThibaultGROUEIX/ChamferDistancePytorch) (for evaluation)

License

[expand] - code: [CC-BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/legalcode). See the [LICENSE](LICENSE) file. - dataset - [CC0](https://creativecommons.org/share-your-work/public-domain/cc0/): cat-pikachiu, cat-coco, dog-tetres, human-cap - [Pexels free license](https://www.pexels.com/license/): penguin - [Turbosquid license](https://blog.turbosquid.com/turbosquid-3d-model-license/): [hands](https://www.turbosquid.com/3d-models/hand-hdri-shader-3d-model-1311775), [eagle](https://www.turbosquid.com/3d-models/eagle-rigged-fbx-free/1045001) - the final dataset is modified from those 3D assets. - [AMA](https://people.csail.mit.edu/drdaniel/mesh_animation/) comes without a license - We thank the artists for sharing theirs videos and 3D assets.