Forecasting Future Videos from Novel Views via Disentangled 3D Scene Representation (2407.21450v2)

Abstract: Video extrapolation in space and time (VEST) enables viewers to forecast a 3D scene into the future and view it from novel viewpoints. Recent methods propose to learn an entangled representation, aiming to model layered scene geometry, motion forecasting and novel view synthesis together, while assuming simplified affine motion and homography-based warping at each scene layer, leading to inaccurate video extrapolation. Instead of entangled scene representation and rendering, our approach chooses to disentangle scene geometry from scene motion, via lifting the 2D scene to 3D point clouds, which enables high quality rendering of future videos from novel views. To model future 3D scene motion, we propose a disentangled two-stage approach that initially forecasts ego-motion and subsequently the residual motion of dynamic objects (e.g., cars, people). This approach ensures more precise motion predictions by reducing inaccuracies from entanglement of ego-motion with dynamic object motion, where better ego-motion forecasting could significantly enhance the visual outcomes. Extensive experimental analysis on two urban scene datasets demonstrate superior performance of our proposed method in comparison to strong baselines.

• The paper introduces a method that separates 3D geometry from motion to forecast future video frames using explicit 3D point clouds.
• It employs a two-stage motion forecasting process, predicting both ego-motion and residual object motion to generate high-fidelity novel views.
• Results on KITTI and Cityscapes show improved SSIM and LPIPS metrics compared to state-of-the-art baselines, highlighting applications in autonomous driving and VR.

Forecasting Future Videos from Novel Views via Disentangled 3D Scene Representation

The paper "Forecasting Future Videos from Novel Views via Disentangled 3D Scene Representation" by Sudhir Yarram and Junsong Yuan proposes a methodology for addressing the combined tasks of video extrapolation in space and time (VEST). This paper introduces an approach that disentangles 3D scene geometry from 3D motion, as well as disentangling ego-motion from dynamic object motion.

Overview of the Approach

The presented method aims to forecast dynamic scenes into the future while enabling the visualization of these scenes from novel viewpoints. This is accomplished by lifting 2D frames into 3D point clouds, which effectively decouples the scene's geometry from its motion. This disentanglement is realized by constructing explicit 3D scene representations using depth maps and subsequently modeling future scene motion through a two-stage forecasting process. This process includes an initial ego-motion prediction followed by the forecasting of residual object motion.

Methodology

The approach comprises three primary steps:

1. Construction of 3D Point Clouds:
   • Depth maps estimated from input frames are utilized to convert 2D images into 3D point clouds. These 3D points encapsulate both spatial location and learned appearance features. Importantly, this step involves semantic segmentation and inpainting of depth maps to handle disocclusions that arise during novel view synthesis.
2. Forecasting Future 3D Motion:
   • The authors propose a two-stage method for motion forecasting:
     • Ego-Motion Forecasting (EMF): This module forecasts the camera's future pose, leveraging the static background information obtained from inpainted feature layers.
     • Object Motion Forecasting (OMF): Using a series of Multi-Scale Motion Flow Blocks (MMFB), this module predicts the residual motion of dynamic objects by focusing on the object-centric features extracted from both the original and inpainted frames.
3. Splatting and Rendering:
   • Following motion forecasting, the adjusted 3D point clouds are rendered into the future frame using point-based rendering techniques. This synthesizes novel views from the desired camera perspectives, leading to the generation of high-fidelity video frames.

Experimental Setup and Results

The paper evaluates the proposed method on two urban scene datasets, KITTI and Cityscapes, examining both short-term and long-term video prediction tasks. Metrics such as SSIM and LPIPS were employed to quantify the quality of the synthesized frames, showing the proposed method's superiority compared to several state-of-the-art baselines including DMVFN, WALDO, and VEST-MPI.

In the task of independent VEST (VEST-[S,T]), the method achieved notable improvements in both video prediction and novel view synthesis. For concurrent VEST (VEST-[S+T]), the results demonstrated that the proposed technique yields sharper and more accurate future frames over extended time horizons, effectively mitigating issues like motion blur and occlusion artifacts that commonly affect prior methods.

Implications and Future Directions

This research provides significant implications for fields requiring accurate dynamic scene modeling such as autonomous driving, virtual reality, and robotic vision. By explicitly leveraging 3D scene geometry and disentangling motion components, the proposed methodology enhances the quality and feasibility of future video extrapolation and novel view synthesis tasks.

Future work might explore several extensions and improvements:

• Enhanced Depth Estimation: Improving the robustness of depth estimation techniques, particularly in challenging scenarios involving thin structures and significant occlusions.
• Real-time Processing: Developing optimizations for real-time applications, crucial for autonomous systems requiring continuous and rapid adaptation to changing environments.
• Incorporating RGB-D Video: Integrating multi-modal data such as RGB-D video streams could further enhance the accuracy and reliability of scene representations.

In summary, this paper presents a comprehensive approach to forecasting future video frames from novel views by leveraging disentangled 3D scene representations. The method shows substantial advancements over current state-of-the-art techniques, fostering new avenues for research and applications in various computer vision domains.