4K4D: Real-Time 4D View Synthesis at 4K Resolution

Repository for our paper 4K4D: Real-Time 4D View Synthesis at 4K Resolution.

News:

24.04.07: We implemented a new tile-based CUDA renderer for 4K4D following 3DGS.
24.04.07: We implemented a WebSocket-based viewer for EasyVolcap along side the native renderer.
24.03.27: The training code for 4K4D has been open-sourced along with documentations.
24.02.27: 4K4D has been accepted to CVPR 2024.
23.12.18: The backbone of 4K4D, our volumetric video framework EasyVolcap has been open-sourced!
23.12.18: The inference code for 4K4D has also been open-sourced along with documentations.

4k4d_dance3_demo.mp4

4k4d_0008_01_demo.mp4

4k4d_0013_01_demo.mp4

For more high-resolution results and more real-time demos, please visit our project page.

Installation

Please refer to the installation guide of EasyVolcap for basic environment setup.

After setting up the environment, you should execute the installation command in this repository's root directory to register the modules:

# Run this inside the 4K4D repo
pip install -e . --no-build-isolation --no-deps

Note that it's not necessary for all requirements present in environment.yml and requirements.txt to be installed on your system as they contain dependencies for other parts of EasyVolcap. Thanks to the modular design of EasyVolcap, this missing packages will not hinder the rendering and training of 4K4D. After the installation process, we're expecting PyTorch, PyTorch3D and tiny-cuda-nn to be present in the current system for the rendering of 4K4D to work properly. For the training of 4K4D, you should also make sure that Open3D is properly installed. Other packages can be easily installed using pip if errors about their import are encountered. Check that this is the case with:

python -c "from easyvolcap.utils.console_utils import *" # Check for easyvolcap installation. 4K4D is a fork of EasyVolcap
python -c "import torch; print(torch.rand(3,3,device='cuda'))" # Check for pytorch installation
python -c "from pytorch3d.io import load_ply" # Check for pytorch3d installation
python -c "import tinycudann" # Check for tinycudann installation
python -c "import open3d" # Check for open3d installation. open3d is only required for training (extracting visual hulls)

Datasets

In this section, we provide instructions on downloading the full dataset for DNA-Rendering, ZJU-Mocap, NHR, ENeRF-Outdoor and Mobile-Stage dataset. If you only want to preview the pretrained models in the interactive GUI without any need for training, we recommend checking out the minimal dataset section because the full datasets are quite large in size. Note that for full quality rendering, you still need to download the full dataset as per the instructions below.

4K4D follows the typical dataset setup of EasyVolcap, where we group similar sequences into sub-directories of a particular dataset. Inside those sequences, the directory structure should generally remain the same: For example, after downloading and preparing the 0013_01 sequence of the DNA-Rendering dataset, the directory structure should look like this:

# data/renbody/0013_01:
# Required:
images # raw images, cameras inside: images/00, images/01 ...
masks # foreground masks, cameras inside: masks/00, masks/01 ...
extri.yml # extrinsic camera parameters, not required if the optimized folder is present
intri.yml # intrinsic camera parameters, not required if the optimized folder is present

# Optional:
optimized # OPTIONAL: optimized camera parameters: optimized/extri.yml, optimized: intri.yml
vhulls # OPTIONAL: extracted visual hull: vhulls/000000.ply, vhulls/000001.ply ... not required if the optimized folder and surfs folder are present
surfs # OPTIONAL: processed visual hull: surfs/000000.ply, surfs/000001.ply ...

DNA-Rendering, NHR and ZJU-MoCap Datasets

Please refer to Im4D's guide to download ZJU-MoCap, NHR and DNA-Rendering datasets. After downloading, the extracted files should be placed in to data/my_zjumocap, data/NHR and data/renbody respectively. If someone is interested in the processed data, please email me at zhenx@zju.edu.cn and CC xwzhou@zju.edu.cn and pengsida@zju.edu.cn to request the processing guide. For ZJU-MoCap, you can fill in this Google form to request the download link. Note that you should cite the corresponding papers if you use these datasets.

ENeRF-Outdoor Dataset

If someone is interested in downloading the ENeRF-Outdoor dataset, please fill in this Google form to request the download link. Note that this dataset is for non-commercial use only. After downloading, the extracted files should be placed in data/enerf_outdoor.

Mobile-Stage Dataset

If someone is interested in downloading the Mobile-Stage dataset, please fill in this Google form to request the download link. Note that this dataset is for non-commercial use only. After downloading, the extracted files should be placed in data/mobile_stage.

Rendering

First, download the pretrained models.

After downloading, place them into data/trained_model (e.g. data/trained_model/4k4d_0013_01/1599.npz, data/trained_model/4k4d_0013_01_r4/latest.pt and data/trained_model/4k4d_0013_01_mb/-1.npz).

Note: The pre-trained models were created with the release codebase. This code base has been cleaned up and includes bug fixes, hence the metrics you get from evaluating them will differ from those in the paper. If yor're interested in reproducing the error metrics reported in the paper, please consider downloading the reference images.

Here we provide their naming convensions which corresponds to their respective config files:

4k4d_0013_01 (without any postfixes) is the real-time 4K4D model, corresponding to configs/projects/realtime4dv/rendering/4k4d_0013_01.yaml. This model can only be used for rendering. When combined with the full dataset mentioned above, this is the full official 4K4D implementation.
4k4d_0013_01_r4 (with the _r4 postfix) is the full pretrained model used during training, corresponding to configs/projects/realtime4dv/training/4k4d_0013_01_r4.yaml. This model can only be used for training. r4 is short for realtime4dv.
4k4d_0013_01_mb (with the _mb postfix) is an extension to 4K4D (Note: to be open-sourced) where we distill the IBR + SH appearance model into a set of low-degree SH parameters. This model can only be used for rendering and do not require pre-computation. mb is short for mobile.

After placing the models and datasets in their respective places, you can run EasyVolcap with configs located in configs/projects/realtime4dv/rendering to perform rendering operations with 4K4D.

For example, to render the 0013_01 sequence of the DNA-Rendering dataset, you can run:

# GUI Rendering
evc-gui -c configs/projects/realtime4dv/rendering/4k4d_0013_01.yaml,configs/specs/vf0.yaml # Only load, precompute and render the first frame
evc-gui -c configs/projects/realtime4dv/rendering/4k4d_0013_01.yaml # Precompute and render all 150 frames, this could take a minute or two

# Testing with input views
evc-test -c configs/projects/realtime4dv/rendering/4k4d_0013_01.yaml,configs/specs/eval.yaml,configs/specs/vf0.yaml # Only render some of the view of the first frame
evc-test -c configs/projects/realtime4dv/rendering/4k4d_0013_01.yaml,configs/specs/eval.yaml # Only rendering some selected testing views and frames

# Rendering rotating novel views
evc-test -c configs/projects/realtime4dv/rendering/4k4d_0013_01.yaml,configs/specs/eval.yaml,configs/specs/spiral.yaml,configs/specs/ibr.yaml,configs/specs/vf0.yaml # Render a static rotating novel view
evc-test -c configs/projects/realtime4dv/rendering/4k4d_0013_01.yaml,configs/specs/eval.yaml,configs/specs/spiral.yaml,configs/specs/ibr.yaml # Render a dynamic rotating novel view

Rendering on Minimal Dataset

We provide a minimal dataset for 4K4D to render with its full pipeline by encoding the input images and masks into videos (typically less than 100MiB each).

This leads to almost no visual quality loss, but if you have access to the full dataset, it's recommended to run the model on the full dataset instead (Sec. Rendering).

Here we provide instructions on setting up the minimal dataset and rendering with it:

Download the pretrained models. If you've already done so for the Rendering section, this step can be skipped.
1. Pretrained models should be placed directly into the data/trained_model directory (e.g. data/trained_model/4k4d_0013_01/1599.npz).
Downlaod the minimal datasets.
1. Place the compressed files inside their respective data_root (e.g. 0013_01_libx265.tar.gz should be placed into data/renbody/0013_01) and uncompressed them.
2. Note that if you've already downloaded the full dataset with raw images as per the Rendering section, there's no need for redownloading the minimal dataset with encoded videos.
3. However, if you continue, no files should be replaced and you can safely run both kinds of rendering.
4. After the uncompression, you should see two folders: videos_libx265 and optimized. The former contains the encoded videos, and the latter contains the optionally optimized camera parameters. For some dataset, you'll see intri.yml and extri.yml instead of the optimized folder. And for some others, you'll see a videos_masks_libx265 for storing the masks separatedly.
Process the minimal datasets using these two scripts:
1. scripts/realtime4dv/extract_images.py: Extract images from the encoded videos. Use --data_root to control which dataset to extract.
2. scripts/realtime4dv/extract_masks.py: Extract masks from the encoded videos. Use --data_root to control which dataset to extract.
3. After the extraction (preprocessing), you should see a images_libx265 and a masks_libx265 inside your data_root. Example processing scripts:

# For foreground datasets with masks and masked images (DNA-Rendering, NHR, ZJU-Mocap)
python scripts/realtime4dv/extract_images.py --data_root data/renbody/0013_01
python scripts/realtime4dv/extract_masks.py --data_root data/renbody/0013_01

# For datasets with masks and full images (ENeRF-Outdoor and dance3 of MobileStage)
python scripts/realtime4dv/extract_images.py --data_root data/mobile_stage/dance3
python scripts/realtime4dv/extract_images.py --data_root data/mobile_stage/dance3 --videos_dir videos_masks_libx265 --images_dir masks_libx265 --single_channel

Now, the minimal dataset has been prepared and you can render a model with it. The only change is to append a new config onto the command: configs/specs/video.yaml. Example rendering scripts:

# See configs/projects/realtime4dv/rendering for more
evc-gui -c configs/projects/realtime4dv/rendering/4k4d_0013_01.yaml,configs/specs/video.yaml # append: ,configs/specs/vf0.yaml to only render the first frame
evc-gui -c configs/projects/realtime4dv/rendering/4k4d_sport2.yaml,configs/specs/video.yaml # append: ,configs/specs/vf0.yaml to only render the first frame
evc-gui -c configs/projects/realtime4dv/rendering/4k4d_my_313.yaml,configs/specs/video.yaml # append: ,configs/specs/vf0.yaml to only render the first frame
evc-gui -c configs/projects/realtime4dv/rendering/4k4d_dance3.yaml,configs/specs/video.yaml # append: ,configs/specs/vf0.yaml to only render the first frame
evc-gui -c configs/projects/realtime4dv/rendering/4k4d_actor1_4.yaml,configs/specs/video.yaml # append: ,configs/specs/vf0.yaml to only render the first frame

Rendering With WebSocket Server & Client

EasyVolcap offers server-side rendering capability with WebSocket. This allows you to render on a remote server and view the results on a local machine. On the local machines, you can freely manipulate the camera and view the results in real-time as if rendering locally.

On the remote server, simply append configs/specs/server.yaml to your normal local rendering command like this:

# Taking NHR sport2 with minimal dataset as an example
evc-gui -c configs/projects/realtime4dv/rendering/4k4d_sport2.yaml,configs/specs/video.yaml,configs/specs/vf0.yaml,configs/specs/server.yaml

On the local client machine, invoke the WebSocket client with evc-ws:

# The config file NHR.yaml provides a initial viewer camera parameter for this dataset
evc-ws -c configs/datasets/NHR/NHR.yaml

# Note that you might need to replace the server ip by passing --host to the command
# Use -- to separate evc parameters with the client ones
evc-ws --host 10.76.5.252 --port 1024 -- -c configs/datasets/NHR/NHR.yaml viewer_cfg.window_size="768,1366"

Using CUDA-Backend for Rendering

We've implemented a CUDA-based tile rasterizer for the point-based representation use in 4K4D.

Install it using:

pip install git+https://github.com/dendenxu/diff-point-rasterization

Note that you might need to first set up a CUDA compilation environment.

After that, simply append model_cfg.sampler_cfg.render_gs=True to your rendering command to enable it.

evc-gui -c configs/projects/realtime4dv/rendering/4k4d_sport2.yaml,configs/specs/vf0.yaml model_cfg.sampler_cfg.render_gs=True

Or you can simply turn it on and off in the viewer under the Rendering tab.

Rendering Without Dataset (Mobile 4K4D)

TODO: Finish up the web viewer for the mobile 4k4d.

Training

Pretrained Models for Training

TODO: Upload pretrained models for continuing training.

Training on DNA-Rendering (ZJU-MoCap and NHR)

Training 4K4D on the prepared dataset is simple, just one command and you're all set. However, it's recommended to train a single-frame version of 4K4D to identify potential issues (5 mins) before running on the full sequence (24 hours). If any missing pacakges are reporteds, please pip install them before going any further. Note that if you've requested the prepared dataset, I should have already prepared the visual hulls and optimized camera parameters for you. If they are not present in the dataset, you should first check the Initialization section of the Custom Dataset section for instructions on how to prepare the visual hulls and optimize the camera parameters (if needed of course).

You can use this script to test the installation and training process of 4K4D. This script trains a single-frame version of 4K4D on the first frame of the 0013_01 sequence of the DNA-Rendering dataset.

evc-train -c configs/exps/4k4d/4k4d_0013_01_r4.yaml,configs/specs/static.yaml,configs/specs/tiny.yaml exp_name=4k4d_0013_01_r4_static

During the first 100-200 iterations, you should see that the training PSNR increase to 24-25 dB. Otherwise there might be bugs during your dataset preparation or installation process.

The actual training of the full model is more straight forward:

evc-train -c configs/exps/4k4d/4k4d_0013_01_r4.yaml

For training on other sequences or dataset, change this line:

- configs/datasets/renbody/0013_01_obj.yaml # dataset usage configuration

to something else like:

- configs/datasets/NHR/sport2_obj.yaml # dataset usage configuration

and save the modified config file separatedly to something like: configs/exps/4k4d/4k4d_sport2_r4.yaml

Example configurations files can be found in configs/exps/4k4d and configs/projects/realtime4dv/training. The 4k4d folder contains some more example configuration files. If you're starting from scratch, it's recommended to use and extend files inside this folder. The training folder contains configuration files matching our provided pretrained model, some of which are legacy model files that requires special configurations. For (a) visual hull initialization and (b) converting the trained models for real-time rendering, please see the Custom Datasets section.

Training on ENeRF-Outdoor

Training 4K4D on a background dataset (dance3 or ENeRF-Outdoor) is a little bit more involved than the foreground-only ones. For the foreground-only ones, you only need to train one model and maybe later convert them for realtime rendering. For the background-enabled ones, you'll need to train a separated model for the background and foreground and then jointly optimize them. Note that if you've requested the processed data, I should have already placed the processed visual hulls and background initializations inside your dataset. If they're not present, please check the Initialization section of the Custom Dataset section for instructions.

Custom Datasets

Dataset Preparation

The 4K4D project is an effort around creating a real-time-renderable neural volumetric video.

In the following, we'll be walking throught the process of training our method on a custom multi-view dataset. Lets call the dataset renbody and call the sequence 0013_01 for notation. Note that you can change out the 0013_01 and renbody parts for other names for you custom dataset. Other namings like 4k4d should remain the same. Let's assume a typical input contains calibrated camera parameters compatible with EasyMocap, where the folder & directory structure looks like this:

data/renbody/0013_01
│── extri.yml
│── intri.yml
├── images
│   ├── 00
│   │   ├── 000000.jpg
│   │   ├── 000001.jpg
│   │   ...
│   │   ...
│   └── 01
│   ...
└── masks
    ├── 00
    │   ├── 000000.jpg
    │   ├── 000001.jpg
    │   ...
    │   ...
    └── 01
    ...

We assume the foreground mask has already been segmented. If not, you could also checkout the scripts contained in scripts/segmentation. My experience is that RobustVideoMatting typically gets the job done. If you've also got access to ground truth background images (with minimal lighting changes), you could also give BackgroundMattingV2 a try. A relevant script can also be found in scripts/segmentation.

Configurations

Given the dataset, you're now prepared for creating your correcponding configuration file for 4K4D. The first file is corresponding to the dataset itself, where data loading paths and input ratios or view numbers are defined. Lets put it in configs/datasets/renbody/0013_01.yaml. You can look at the actual file to get a grasp of what info this file should contain. At the minimum, you should specify the data loading root for the dataset. If you feel unfamilier with the configuration system, feel free to check out the specific documentation for that part. The content of the 0013_01.yaml (and its parent renbody.yaml) file should look something like this:

# Content of configs/datasets/renbody/0013_01.yaml
configs: configs/datasets/renbody/renbody.yaml

dataloader_cfg: # we see the term "dataloader" as one word?
    dataset_cfg: &dataset_cfg
        data_root: data/renbody/0013_01
        images_dir: images_calib

val_dataloader_cfg:
    dataset_cfg:
        <<: *dataset_cfg

# Content of configs/datasets/renbody/renbody.yaml
dataloader_cfg: # we see the term "dataloader" as one word?
    dataset_cfg: &dataset_cfg
        masks_dir: masks
        ratio: 0.5
        bounds: [[-5.0, -5.0, -5.0], [5.0, 5.0, 5.0]] # thinner?

        force_sparse_view: True
        view_sample: [0, 60, 1]
        frame_sample: [0, 150, 1] # only train for a thousand frames

model_cfg:
    sampler_cfg:
        bg_brightness: 0.0
    renderer_cfg:
        bg_brightness: 0.0

val_dataloader_cfg:
    dataset_cfg:
        <<: *dataset_cfg
        frame_sample: [0, 150, 1]
    sampler_cfg:
        view_sample: [0, 60, 20]

Here you'll see I created a general description file configs/datasets/renbody/renbody.yaml for the whole DNA-Rendering dataset, which is a good practice if your multi-view dataset contains multiple different sequences but they are under roughly the same setting (view count, lighting condition, mask quality etc.). You'll also note I explicitly specified how many views and frames this dataset has. The number you put in here should not exceed the actual amount. If you're feeling lazy you can also just write [0, null, 1] for view_sample and frame_sample, however doing this means a trained model will still require access to the original dataset to perform some loading and rendering.

Until now, such data preparation are generalizable across all multi-view dataset supported by EasyVolcap, you should always create the corresponding dataset configuraitons for you custom ones as this helps in reproducibility.

Our next step is to create the corresponding 4K4D configuration for running experiments on the 0013_01 sequence. You can create a configs/exps/4k4d/4k4d_0013_01_r4.yaml to hold such information:

configs:
    - configs/base.yaml # default arguments for the whole codebase
    - configs/models/r4dv.yaml # network model configuration
    - configs/datasets/renbody/0013_01_obj.yaml # dataset usage configuration
    - configs/specs/mask.yaml # specific usage configuration
    - configs/specs/optimized.yaml # specific usage configuration

# prettier-ignore
exp_name: {{fileBasenameNoExtension}}

You'll notice I placed the configurations in and order of base, model, dataset and then specs. This is typically the best practice as you get more and more specific about the experiment you want to perform here. The mask.yaml file will provide necessary configurations for reading, parsing and using provided foreground masks, and it is essential for all experiments on 4K4D.

For some datasets (like 4k4d_sport2_r4.yaml), you'll notice I included a spec called prior.yaml. The prior.yaml file slightly modifies the optimizaion hyperparameters (increase weight of input masks and increate input view count) since the mask of NHR is quite accurate and the focal length is quite high. You can also check out the actual file to get a better understanding of what it does (this applies to almost all files under the specs directory).

For now, there's no 0013_01_obj.yaml. This file should contain a tighter bounding box aggregated from the visual hulls and we will describe how to prepare this in the next section.

Initialization (Space Carving || Visual Hull)

The next step for 4K4D is initialization. With the correct dataset placement and configuration files in place, you should be able to run the following script to extract the visull hull from input foreground masks:

# Extract visual hulls
evc-test -c configs/base.yaml,configs/models/r4dv.yaml,configs/datasets/renbody/0013_01.yaml,configs/specs/optimized.yaml,configs/specs/vhulls.yaml

# Preprocess visual hulls
evc-test -c configs/base.yaml,configs/models/r4dv.yaml,configs/datasets/renbody/0013_01.yaml,configs/specs/optimized.yaml,configs/specs/surfs.yaml

This won't take long. Typically a few minutes should suffices. After which you'll notice a folder vhulls created in your dataset folder (data/renbody/renbody in this example). Note that we also support directly running the training script and let EasyVolcap lazily take care of this initialization process, but since 4K4D could benefit from a tighter bounding box when defininig its 4D feature volumes (two modified K-Planes), I figure one extra hoop should be worth it.

TODO: Make this bbox summarization process automatic

You‘ll also notice the above script will output an aggregated bounding box value. Copy it and create a new file configs/datasets/renbody/0013_01_obj.yaml to hold it.

configs: configs/datasets/renbody/0013_03.yaml

dataloader_cfg: &dataloader_cfg
    dataset_cfg: &dataset_cfg # ratio: 0.5
        bounds: [[-0.5152, -0.6097, -0.9667], [0.5948, 0.8103, 0.7933]] # !: BATCH
        vhull_thresh: 0.90

val_dataloader_cfg:
    dataset_cfg:
        <<: *dataset_cfg

And modify your configs/exps/4k4d/4k4d_0013_01_r4.yaml to replace 0013_01.yaml with 0013_01_obj.yaml. This will make sure we use the tightest bound when defining the dimensions of our 4D feature volumes (two modified K-Planes).

Training

Next, we're ready to perform the actual training:

evc-train -c configs/exps/4k4d/4k4d_0013_01_r4.yaml

This will take a day two depending on your machine. Please pay attention to the console logs and keep and eye out for the loss and metrics. All records and training time evalution will be saved to data/record and data/result respectively. So launch your tensorboard or other viewing tools for training inspection. After training, we'd want to perform post processing on the trained model for various uses.

Rendering

Firstly, we want to generate the version that supports realtime rendering while maintaining almost no performance loss (dubbed super charged):

python scripts/realtime4dv/charger.py --sampler SuperChargedR4DV --exp_name 4k4d_0013_01 -- -c configs/exps/4k4d/4k4d_0013_01_r4.yaml,configs/specs/super.yaml

Now you can render the converted model in the interactive EasyVolcap viewer with:

evc-gui -c configs/exps/4k4d/4k4d_0013_01_r4.yaml,configs/specs/superf.yaml exp_name=4k4d_0013_01

The preloading of all frames is quite heavy on system memory and also time intensive. So we advise controlling the number of frames to load via:

evc-gui -c configs/exps/4k4d/4k4d_0013_01_r4.yaml,configs/specs/superf.yaml,configs/specs/vf0.yaml exp_name=4k4d_0013_01

0 is the starting frame, 1 is the ending frame id and 1 indicates how many frames to jump. For example, 0,30,5 will load 6 frames (0,5,10,15,20,25). You can also use None to indicate the default value for the total. For example, val_dataloader_cfg.dataset_cfg.frame_sample=0,None,1 will load all frames. Note that this variant is the default variant used in the paper.

Optimizing the Cameras

Inheriting from EasyVolcap, 4K4D also supports loading optimized camera parameters for each dataset (sequence). This is done by training an improved Instant-NGP model on one frame of the sequence. And then extracting the optimized camera parameters to the EasyVolcap format. However, if your dataset's provided camera parameters are already good enough, you can skip this step. For the NHR dataset, the provided cameras are good enough thus we did not mention this step in the previous sections. So, from now on, we will be using the 0013_01 sequence of the DNA-Rendering dataset as an example.

More details on how to perform the camera parameters finetune can be found in camera.md. For now, let's assume you've got a l3mhet_0013_01_static.yaml file setup and ready for use.

After training the single frame camera optimization model using this script:

evc-train -c configs/specs/exps/l3mhet/l3mhet_0013_01_static.yaml

You're expected to finded a model data/trained_model/l3mhet_0013_01_static.yaml

TODO: Continues this.

Custom Full-Scene Datasets

Prepare the dynamic dataset as in custom_dataset.md. Specifically, you should follow the guide in the dense calibration section to prepare the optimized camera parameters using NGP (optimized). And then, you can follow the guide in the space carving section to prepare the visual hulls (vhulls and surfs). After that, we can continue on the training of custom full-scene dataset.

First of all, rearrange the background images folder and train a background NGP:

# Prepare dataset variables
expname=actor1_4
data_root=data/enerf_outdoor/actor1_4

# Rearrange background images
python scripts/segmentation/link_backgrounds.py --data_root ${data_root}

# Train background NGP
evc-train -c configs/exps/l3mhet/l3mhet_${expname}_bkgd.yaml

# Extract point clouds from trained background NGP
python scripts/fusion/volume_fusion.py --skip_depth_consistency -- -c configs/exps/l3mhet/l3mhet_${expname}_bkgd.yaml val_dataloader_cfg.dataset_cfg.ratio=0.15 val_dataloader_cfg.dataset_cfg.view_sample=0,None,3 # 50W should be ok

# Prepare for initialization of bg 4k4d
mkdir -p ${data_root}/bkgd/boost
cp data/geometry/l3mhet_${expname}_bkgd/POINT/frame0000.ply ${data_root}/bkgd/boost/000000.ply

Prepare 3 configs like:

4k4d_actor1_4_r4_bg.yaml: Background 4K4D training config
4k4d_actor1_4_r4_fg.yaml: Foreground 4K4D training config
4k4d_actor1_4_r4.yaml: Joint 4K4D training config

Optionally, create static version of the fg model to validate the implementation like 4k4d_actor1_4_r4_fg_static.yaml:

# Train static foreground model
evc-train -c configs/exps/4k4d/4k4d_${expname}_r4_fg_static.yaml

And optionally, validate the rendering of the static frame:

# Convert to real-time format
python scripts/realtime4dv/charger.py --exp_name 4k4d_${expname}_fg_static --sampler SuperChargedR4DV -- -c configs/exps/4k4d/4k4d_${expname}_r4_fg_static.yaml,configs/specs/super.yaml

# Real-time rendering in GUI
evc-gui -c configs/exps/4k4d/4k4d_${expname}_r4_fg_static.yaml,configs/specs/superf.yaml exp_name=4k4d_${expname}_fg_static

Train the bg and fg model seperatedly:

# Train background model
evc-train -c configs/exps/4k4d/4k4d_${expname}_r4_bg.yaml

# Train foreground model, this could take a long time
evc-train -c configs/exps/4k4d/4k4d_${expname}_r4_fg.yaml

# Joint training
evc-train -c configs/exps/4k4d/4k4d_${expname}_r4.yaml

Real-time rendering of backgrounds:

# Convert to real-time format
python scripts/realtime4dv/charger.py --exp_name 4k4d_${expname}_bg --sampler SuperChargedR4DV -- -c configs/exps/4k4d/4k4d_${expname}_r4_bg.yaml,configs/specs/super.yaml

# Non-real-time rendering
evc-test -c configs/exps/4k4d/4k4d_${expname}_r4_bg.yaml,configs/specs/spiral.yaml,configs/specs/ibr.yaml val_dataloader_cfg.dataset_cfg.focal_ratio=0.65 val_dataloader_cfg.dataset_cfg.n_render_views=600

# Real-time rendering of spiral path
evc-test -c configs/exps/4k4d/4k4d_${expname}_r4_bg.yaml,configs/specs/superf.yaml,configs/specs/spiral.yaml,configs/specs/ibr.yaml,configs/specs/eval.yaml exp_name=4k4d_${expname}_bg val_dataloader_cfg.dataset_cfg.focal_ratio=0.65 val_dataloader_cfg.dataset_cfg.n_render_views=600

# Real-time rendering in GUI
evc-gui -c configs/exps/4k4d/4k4d_${expname}_r4_bg.yaml,configs/specs/superf.yaml exp_name=4k4d_${expname}_bg

Real-time rendering of foregrounds:

# Convert to real-time format
python scripts/realtime4dv/charger.py --exp_name 4k4d_${expname}_fg --sampler SuperChargedR4DV -- -c configs/exps/4k4d/4k4d_${expname}_r4_fg.yaml,configs/specs/super.yaml

# Non-real-time rendering
evc-test -c configs/exps/4k4d/4k4d_${expname}_r4_fg.yaml,configs/specs/spiral.yaml,configs/specs/ibr.yaml val_dataloader_cfg.dataset_cfg.focal_ratio=0.65 val_dataloader_cfg.dataset_cfg.n_render_views=600

# Real-time rendering of spiral path
evc-test -c configs/exps/4k4d/4k4d_${expname}_r4_fg.yaml,configs/specs/superf.yaml,configs/specs/spiral.yaml,configs/specs/ibr.yaml,configs/specs/eval.yaml exp_name=4k4d_${expname}_fg val_dataloader_cfg.dataset_cfg.focal_ratio=0.65 val_dataloader_cfg.dataset_cfg.n_render_views=600

# Real-time rendering in GUI
evc-gui -c configs/exps/4k4d/4k4d_${expname}_r4_fg.yaml,configs/specs/superf.yaml exp_name=4k4d_${expname}_fg

Real-time rendering of joint results:

# Convert to real-time format
python scripts/realtime4dv/charger.py --exp_name 4k4d_${expname} --sampler SuperChargedR4DVB -- -c configs/exps/4k4d/4k4d_${expname}_r4.yaml,configs/specs/super.yaml

# Non-real-time rendering
evc-test -c configs/exps/4k4d/4k4d_${expname}_r4.yaml,configs/specs/spiral.yaml,configs/specs/ibr.yaml val_dataloader_cfg.dataset_cfg.focal_ratio=0.65 val_dataloader_cfg.dataset_cfg.n_render_views=600

# Real-time rendering of spiral path
evc-test -c configs/exps/4k4d/4k4d_${expname}_r4.yaml,configs/specs/superf.yaml,configs/specs/spiral.yaml,configs/specs/ibr.yaml,configs/specs/eval.yaml exp_name=4k4d_${expname} val_dataloader_cfg.dataset_cfg.focal_ratio=0.65 val_dataloader_cfg.dataset_cfg.n_render_views=600

# Real-time rendering in GUI
evc-gui -c configs/exps/4k4d/4k4d_${expname}_r4.yaml,configs/specs/superb.yaml exp_name=4k4d_${expname}

Using static scene to check implementation and avoid OOM errors:

# Run training on first frame
evc-train -c configs/exps/4k4d/4k4d_${expname}_r4_static.yaml

# Run rendering on first frame of non-real-time model
evc-test -c configs/exps/4k4d/4k4d_${expname}_r4_static.yaml

# Convert to real-time model
python scripts/realtime4dv/charger.py --exp_name 4k4d_${expname}_static --sampler SuperChargedR4DVB -- -c configs/exps/4k4d/4k4d_${expname}_r4_static.yaml,configs/specs/super.yaml

# Run rendering with real-time model
evc-test -c configs/exps/4k4d/4k4d_${expname}_r4_static.yaml,configs/specs/spiral.yaml,configs/specs/ibr.yaml,configs/specs/superb.yaml,configs/specs/eval.yaml exp_name=4k4d_${expname}_static

# Run GUI rendering with real-time model
evc-gui -c configs/exps/4k4d/4k4d_${expname}_r4_static.yaml,configs/specs/superb.yaml exp_name=4k4d_${expname}_static

Acknowledgements

We would like to acknowledge the following inspiring prior work:

EasyVolcap: Accelerating Neural Volumetric Video Research (Xu et al.)
IBRNet: Learning Multi-View Image-Based Rendering (Wang et al.)
ENeRF: Efficient Neural Radiance Fields for Interactive Free-viewpoint Video (Lin et al.)
K-Planes: Explicit Radiance Fields in Space, Time, and Appearance (Fridovich-Keil et al.)

Citation

If you find this code useful for your research, please cite us using the following BibTeX entry.

@inproceedings{xu20234k4d,
  title={4K4D: Real-Time 4D View Synthesis at 4K Resolution},
  author={Xu, Zhen and Peng, Sida and Lin, Haotong and He, Guangzhao and Sun, Jiaming and Shen, Yujun and Bao, Hujun and Zhou, Xiaowei},
  booktitle={CVPR},
  year={2024}
}

@article{xu2023easyvolcap,
  title={EasyVolcap: Accelerating Neural Volumetric Video Research},
  author={Xu, Zhen and Xie, Tao and Peng, Sida and Lin, Haotong and Shuai, Qing and Yu, Zhiyuan and He, Guangzhao and Sun, Jiaming and Bao, Hujun and Zhou, Xiaowei},
  booktitle={SIGGRAPH Asia 2023 Technical Communications},
  year={2023}
}

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readme.md

readme.md

4K4D: Real-Time 4D View Synthesis at 4K Resolution

Installation

Datasets

DNA-Rendering, NHR and ZJU-MoCap Datasets

ENeRF-Outdoor Dataset

Mobile-Stage Dataset

Rendering

Rendering on Minimal Dataset

Rendering With WebSocket Server & Client

Using CUDA-Backend for Rendering

Rendering Without Dataset (Mobile 4K4D)

Training

Pretrained Models for Training

Training on DNA-Rendering (ZJU-MoCap and NHR)

Training on ENeRF-Outdoor

Custom Datasets

Dataset Preparation

Configurations

Initialization (Space Carving || Visual Hull)

Training

Rendering

Optimizing the Cameras

Custom Full-Scene Datasets

Acknowledgements

Citation

Files

readme.md

Latest commit

History

readme.md

File metadata and controls

4K4D: Real-Time 4D View Synthesis at 4K Resolution

Installation

Datasets

DNA-Rendering, NHR and ZJU-MoCap Datasets

ENeRF-Outdoor Dataset

Mobile-Stage Dataset

Rendering

Rendering on Minimal Dataset

Rendering With WebSocket Server & Client

Using CUDA-Backend for Rendering

Rendering Without Dataset (Mobile 4K4D)

Training

Pretrained Models for Training

Training on DNA-Rendering (ZJU-MoCap and NHR)

Training on ENeRF-Outdoor

Custom Datasets

Dataset Preparation

Configurations

Initialization (Space Carving || Visual Hull)

Training

Rendering

Optimizing the Cameras

Custom Full-Scene Datasets

Acknowledgements

Citation