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Instant-NSR (Pytorch)

A Pytorch implementation of Instant-NSR, fast surface reconstructor as described in Human Performance Modeling and Rendering via Neural Animated Mesh.

Human Performance Modeling and Rendering via Neural Animated Mesh
ACM Transactions on Graphics (SIGGRAPH Asia 2022)
Project page / Paper / Video

<img src="assets/pipeline.jpg" height="260"/> Based on dense multi-view input, our approach enables efficient and high-quality reconstruction, compression, and rendering of human performances. It supports 4D photo-real content playback for various immersive experiences of human performances in virtual and augmented reality.

This repo helps us to reconstruct 3D models from multi-view images in ~10 mins. Free to run our code!

Install

First, you need to set training

pip install -r requirements.txt

# (optional) install the tcnn backbone
pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch

Tested on Ubuntu with torch 1.10 & CUDA 11.4 on RTX 3090.

Usage

We use the same data format as nerf and instant-ngp, and we provide a test dataset dance which is on google driver. Please download and put it under {INPUTS}/dance and then run our Instant-NSR code.

First time running will take some time to compile the CUDA extensions.

Train your own models, you can run following shell:

# Instant-NSR Training
CUDA_VISIBLE_DEVICES=${CUDA_DEVICE} python train_nerf.py "${INPUTS}/dance"  --workspace "${WORKSPACE}" --downscale 1 --network sdf

Then, you can extract surface from the trained network model by:

# Instant-NSR Mesh extraction
CUDA_VISIBLE_DEVICES=${CUDA_DEVICE} python train_nerf.py "${INPUTS}/dance"  --workspace "${WORKSPACE}" --downscale 1 --network sdf -mode mesh

Or, you can render target view with spefic camera view:

# Instant-NSR Rendering
CUDA_VISIBLE_DEVICES=${CUDA_DEVICE} python train_nerf.py "${INPUTS}/dance"  --workspace "${WORKSPACE}" --downscale 1 --network sdf -mode render

Results

Here are some reconstruction results from our Instant-NSR code: <img src="assets/results.gif" height="400"/>

Acknowledgement

Our code is implemented on torch-ngp code base:

@misc{torch-ngp,
    Author = {Jiaxiang Tang},
    Year = {2022},
    Note = {https://github.com/ashawkey/torch-ngp},
    Title = {Torch-ngp: a PyTorch implementation of instant-ngp}
}