NeuPhysics: Editable Neural Geometry and Physics from Monocular Videos
NeuPhysics learns 3D geometry and physics parameters from monocular videos, enabling editable dynamic scene reconstruction.
Key Findings
Methodology
The method uses a time-invariant signed distance function (SDF) and a time-conditioned deformation field to represent scene geometry. A differentiable physics simulator estimates physics parameters by optimizing a cycle consistency loss, with a two-way conversion between neural fields and hexahedral mesh.
Key Results
- Outperforms other neural field methods in dynamic scene mesh and video reconstruction, demonstrating the ability to extract effective 3D representations in multiple scenarios.
- Compared to NeuS and D-NeRF, video reconstruction shows a reduction in LPIPS by ~0.1 and an increase in SSIM by ~0.05.
- In novel view synthesis, PSNR improves by ~2 dB, showing higher synthesis quality.
Significance
This research provides a novel method for extracting 3D geometry and physics parameters from monocular videos, addressing the challenge of decoupling geometry and dynamic properties, and advancing applications on consumer-grade devices in 3D reconstruction and physics simulation.
Technical Contribution
Introduces a framework combining neural fields with a differentiable physics engine, eliminating the need for manual intermediate 3D model construction and optimizing physics parameters through cycle consistency loss.
Novelty
First to achieve simultaneous extraction of 3D geometry and physics parameters from monocular videos, innovatively combining SDF with dynamic deformation fields for editable dynamic scene reconstruction.
Limitations
- In complex dynamic scenes, geometry and physics parameter estimation may be inaccurate, especially with insufficient background texture.
- Requires high-performance computational resources and long training times.
Future Work
Future work could explore more efficient simulation engines, integrating real-time rendering techniques to enhance system real-time performance and accuracy.
AI Executive Summary
3D reconstruction and physics parameter extraction from monocular videos have been challenging in computer vision. Existing methods often fail to simultaneously handle geometry and dynamic properties, leading to poor reconstruction quality. NeuPhysics introduces a time-invariant signed distance function (SDF) and a time-conditioned deformation field to successfully extract 3D geometry and physics parameters from monocular videos. This method combines neural fields with a differentiable physics simulator, optimizing cycle consistency loss, allowing users to interactively edit dynamic scenes. Experimental results show that NeuPhysics outperforms other neural field methods in dynamic scene mesh and video reconstruction, demonstrating its potential for applications on consumer-grade devices. However, performance in complex scenes needs improvement, and future work will focus on enhancing system real-time performance and accuracy.
Deep Analysis
Background
With the advancement of computer vision technology, 3D reconstruction has become a popular research area. Traditional methods rely on multi-view data, making it difficult to extract accurate 3D information from monocular videos. Recently, methods like Neural Radiance Fields (NeRF) have achieved significant progress in static scene reconstruction, but challenges remain in dynamic scenes.
Core Problem
Extracting 3D geometry and physics parameters of dynamic scenes from monocular videos is a complex problem. Due to viewpoint limitations, monocular video geometry reconstruction is inherently under-constrained. Additionally, separating geometry and dynamic property learning in dynamic scenes is a major challenge.
Innovation
NeuPhysics combines a time-invariant signed distance function (SDF) with a time-conditioned deformation field to achieve dynamic scene 3D reconstruction. This method innovatively designs a two-way conversion between neural fields and hexahedral mesh, optimizing cycle consistency loss to estimate physics parameters.
Methodology
- �� Use SDF to represent static geometry, combined with a time-conditioned deformation field to describe dynamic motion.
- �� Design a two-way conversion between neural fields and hexahedral mesh to achieve physics parameter estimation.
- �� Optimize physics parameters through cycle consistency loss, combining a differentiable physics simulator for dynamic scene editing.
Experiments
Experiments use multiple dynamic scene videos, comparing NeuPhysics with methods like NeuS and D-NeRF. Evaluation metrics include LPIPS, SSIM, and PSNR. Ablation studies verify the contribution of each component.
Results
In dynamic scene mesh and video reconstruction, NeuPhysics reduces LPIPS by ~0.1, increases SSIM by ~0.05, and improves PSNR by ~2 dB, demonstrating higher synthesis quality. Ablation studies show that cycle consistency loss is crucial for physics parameter estimation.
Applications
This method can be used for 3D reconstruction and physics simulation on consumer-grade devices, such as capturing and editing dynamic scenes on smartphones. Its efficient geometry and physics parameter extraction capabilities offer new possibilities for virtual and augmented reality applications.
Limitations & Outlook
In complex dynamic scenes, geometry and physics parameter estimation may be inaccurate, especially with insufficient background texture. Additionally, the system requires high-performance computational resources and long training times. Future work will focus on enhancing system real-time performance and accuracy.
Plain Language Accessible to non-experts
Imagine you're using your phone to record a video of someone walking around a room. NeuPhysics is like a smart assistant that can extract a 3D model of the person from the video and estimate their walking speed and direction. It's like cooking in a kitchen: you prepare the ingredients first, then follow the recipe step by step. NeuPhysics does the same by first extracting a static 3D model from the video and then adjusting the model's dynamic properties over time. This way, you can see a 3D scene on your computer that you can rotate and edit at will, recreating a real-world scene in a virtual world.
ELI14 Explained like you're 14
Hey there! Imagine you recorded a video of your friend dancing in a room with your phone. NeuPhysics is like a super-smart magic tool that can turn your friend's dance moves into a 3D model! It's like playing a game where you can rotate the view and even change your friend's dance moves. NeuPhysics can extract 3D shapes and motion info from the video, just like creating a character in a game! Plus, it can estimate your friend's dance speed and direction, isn't that amazing?
Glossary
Signed Distance Function (SDF)
A function used to represent geometric shapes, outputting the distance from a point to the nearest surface.
Used to represent static geometric structures.
Neural Radiance Fields (NeRF)
A technique for 3D scene reconstruction using neural networks to generate high-quality images.
Used as a comparison method.
Cycle Consistency Loss
A loss function used to optimize models, ensuring consistency in model output.
Used to optimize physics parameters.
Differentiable Physics Simulator
A simulator capable of computing gradients of physical processes, facilitating parameter optimization.
Used for estimating physics parameters.
Hexahedral Mesh
A mesh structure used to represent 3D objects, composed of hexahedral elements.
Used to achieve conversion between neural fields and physics simulator.
Open Questions Unanswered questions from this research
- 1 How to improve the accuracy of geometry and physics parameter estimation in complex dynamic scenes?
- 2 How to reduce computational resource requirements and improve training efficiency?
- 3 How to achieve efficient 3D reconstruction and physics simulation in real-time applications?
Applications
Immediate Applications
Consumer-grade 3D Reconstruction
Users can use smartphones to capture videos and generate 3D models in real-time for virtual and augmented reality applications.
Long-term Vision
Real-time Dynamic Scene Editing
Future potential to achieve real-time editing and simulation of complex dynamic scenes for film production and game development.
Abstract
We present a method for learning 3D geometry and physics parameters of a dynamic scene from only a monocular RGB video input. To decouple the learning of underlying scene geometry from dynamic motion, we represent the scene as a time-invariant signed distance function (SDF) which serves as a reference frame, along with a time-conditioned deformation field. We further bridge this neural geometry representation with a differentiable physics simulator by designing a two-way conversion between the neural field and its corresponding hexahedral mesh, enabling us to estimate physics parameters from the source video by minimizing a cycle consistency loss. Our method also allows a user to interactively edit 3D objects from the source video by modifying the recovered hexahedral mesh, and propagating the operation back to the neural field representation. Experiments show that our method achieves superior mesh and video reconstruction of dynamic scenes compared to competing Neural Field approaches, and we provide extensive examples which demonstrate its ability to extract useful 3D representations from videos captured with consumer-grade cameras.