STaR: Self-supervised Tracking and Reconstruction of Rigid Objects in Motion with Neural Rendering
STaR employs self-supervised neural radiance fields to reconstruct and track rigid objects in motion from multi-view videos, enabling photorealistic novel view synthesis without labels.
Key Findings
Methodology
STaR utilizes two independent NeRF models, one static and one dynamic, optimized jointly with rigid motion parameters ξ(t) using Lie group methods. The static NeRF encodes the scene background, while the dynamic NeRF models moving objects. During training, the system employs volumetric rendering formulas, entropy regularization, and importance sampling to disentangle scene components. The rigid pose ξ(t) aligns the dynamic NeRF with the static scene across time, enabling accurate scene decomposition and novel view synthesis. The entire framework is trained in an online manner, allowing adaptation to long videos. The optimization minimizes photometric loss with regularization to prevent entanglement, resulting in high-fidelity reconstructions and animations.
Key Results
- On synthetic datasets like Blender-rendered lamp and desk, PSNR reaches 32.95, SSIM 0.957, LPIPS 0.023, outperforming NeRF, NeRF-time, and NeRF-W, especially in dynamic regions. On real-world data, such as moving banana videos, the method effectively separates background and moving objects, enabling realistic novel view synthesis and unseen trajectory animation. Ablation studies confirm the importance of initialization, online training, and regularization, with performance drops observed when these components are removed.
- The model demonstrates superior spatial-temporal rendering quality, capable of producing photorealistic images from novel viewpoints and times, including interpolated frames. It supports the synthesis of unseen object trajectories, showcasing its potential for virtual scene editing and animation. Quantitative metrics indicate significant improvements over baselines, with PSNR gains of over 7 points in some cases.
- The approach's ability to disentangle static and dynamic components without supervision marks a breakthrough, opening avenues for autonomous scene understanding, virtual environment creation, and interactive applications. Its robustness across synthetic and real data underscores its practical relevance.
Significance
This work advances neural rendering by enabling self-supervised, high-fidelity reconstruction of dynamic scenes with rigid objects. It addresses longstanding challenges in scene decomposition, motion tracking, and novel view synthesis without manual labels. The method's capacity to animate unseen trajectories and separate scene components paves the way for more autonomous, scalable virtual environment generation. Its implications span virtual reality, film production, and robotics, where understanding and manipulating dynamic scenes are crucial. By bridging the gap between static scene modeling and real-world motion, STaR sets a new benchmark for scene understanding in computer vision and graphics.
Technical Contribution
The core innovation lies in combining dual NeRF models with Lie group-based rigid pose optimization, enabling unsupervised scene decomposition and motion tracking. The framework introduces a novel regularization scheme, including entropy constraints, to disentangle static and dynamic scene parts effectively. It supports high-quality novel view synthesis and animation, even for unseen trajectories. This approach extends the capabilities of neural radiance fields from static scenes to complex dynamic environments, offering a flexible, end-to-end trainable system that can handle real-world videos with significant motion and appearance variations.
Novelty
This is the first work to achieve self-supervised, neural rendering-based scene decomposition and dynamic scene reconstruction solely from multi-view videos. Unlike prior methods relying on labels or explicit segmentation, STaR leverages a factorized NeRF framework with rigid pose optimization, enabling automatic separation of static and moving parts. Its ability to animate unobserved trajectories without supervision distinguishes it from existing approaches like NeRF-time or NeRF-W, marking a significant leap in dynamic scene understanding.
Limitations
- Currently limited to scenes with a single rigid object; handling multiple interacting objects or non-rigid deformations remains challenging. The optimization process is computationally intensive, requiring extensive GPU resources and training time (~55 hours). The model's performance may degrade under extreme lighting changes or highly non-rigid motions, indicating the need for more sophisticated motion models and regularization strategies in future work.
- The method assumes scene rigidity and may not generalize well to scenes with complex non-rigid deformations or multiple objects. Real-time applications are limited due to high computational costs. Further research is needed to extend the framework to handle diverse scene dynamics and improve efficiency.
Future Work
Future directions include extending the model to multi-object scenes with non-rigid deformations, improving computational efficiency for real-time applications, and integrating semantic understanding for scene editing. Exploring unsupervised learning of complex motions and scene hierarchies will further enhance scene comprehension. Additionally, combining this approach with large-scale datasets and real-time tracking could revolutionize virtual environment creation and interaction.
AI Executive Summary
Deep Dive
Plain Language Accessible to non-experts
想象你在看一场电影，电影中的人物和场景不断变化，但你只用一台普通相机拍摄了多角度的录像。传统的方法就像拼图，只能拼出静止的画面，不能让你看到运动的全过程。而这项技术就像给你的相机装上了“神奇的眼睛”，它可以自动分辨场景中的静态背景和运动中的人物，甚至预测未来会怎么动。更酷的是，它不需要你提前告诉它怎么做，只用这些录像，就能自己学会分辨场景中的静态和动态部分，还能让你用不同角度看场景，甚至让人物做出你没见过的新动作。就像你有了一个超级聪明的机器人朋友，能帮你制作动画、虚拟世界，甚至预测未来的场景变化。这让虚拟世界变得更真实、更有趣，也更容易让每个人都能用得上。
ELI14 Explained like you're 14
你知道在玩游戏或者看动画时，有时候场景里的东西会动、会变，但你只能用一台相机拍摄几张照片，然后试图想象它们是怎么动的。以前的技术就像拼图，只能拼出静止的画面，不能让你看到运动的全过程。现在，这项新技术就像给你的相机装上了“神奇的眼睛”，它可以看出场景里静止的背景和运动中的物体，还能预测它们未来会怎么动。更酷的是，它不需要你提前告诉它怎么做，只用这些录像，就能自己学会分辨场景中的静态和动态部分，还能让你用不同角度看场景，甚至让物体做出你没见过的新动作。就像你有了一个超级聪明的机器人朋友，能帮你制作动画、虚拟世界，甚至预测未来的场景变化。这让虚拟世界变得更真实、更有趣，也更容易让每个人都能用得上。
Abstract
We present STaR, a novel method that performs Self-supervised Tracking and Reconstruction of dynamic scenes with rigid motion from multi-view RGB videos without any manual annotation. Recent work has shown that neural networks are surprisingly effective at the task of compressing many views of a scene into a learned function which maps from a viewing ray to an observed radiance value via volume rendering. Unfortunately, these methods lose all their predictive power once any object in the scene has moved. In this work, we explicitly model rigid motion of objects in the context of neural representations of radiance fields. We show that without any additional human specified supervision, we can reconstruct a dynamic scene with a single rigid object in motion by simultaneously decomposing it into its two constituent parts and encoding each with its own neural representation. We achieve this by jointly optimizing the parameters of two neural radiance fields and a set of rigid poses which align the two fields at each frame. On both synthetic and real world datasets, we demonstrate that our method can render photorealistic novel views, where novelty is measured on both spatial and temporal axes. Our factored representation furthermore enables animation of unseen object motion.