Generating Dynamic 3D Scenes from Monocular Videos with Multiple Moving Objects

Khái niệm cốt lõi

DreamScene4D can generate realistic 4D scenes from monocular videos with multiple dynamic objects and large motions, by decomposing the scene, factorizing the 3D motion, and composing the objects guided by monocular depth.

Tóm tắt

The paper presents DreamScene4D, a novel approach for generating dynamic 4D scenes from monocular videos with multiple moving objects. The key contributions are:

Video Scene Decomposition: The method first decomposes the input video into individual objects and the background, using zero-shot mask trackers and an adapted diffusion model for amodal video completion to handle occlusions.

3D Motion Factorization: The 3D motion of each object is factorized into three components - object-centric deformation, object-to-world transformation, and camera motion. This factorization greatly improves the stability and quality of the optimization process.

4D Scene Composition: The individually optimized 4D Gaussians representing the objects are composed into a unified coordinate frame using monocular depth guidance to determine the relative scale and placement of the objects.

The method is evaluated on challenging real-world datasets like DAVIS and Kubric, showing significant improvements over existing video-to-4D approaches in terms of both rendering quality and 3D motion accuracy. DreamScene4D can also enable accurate 2D point tracking by projecting the inferred 3D trajectories, without explicit training for this task.

Thống kê

The input videos can contain multiple dynamic objects with large motions and occlusions.
DreamScene4D achieves a CLIP score of 85.09 and LPIPS of 0.152 on the DAVIS dataset, and a CLIP score of 85.53 and LPIPS of 0.112 on the Kubric dataset.
On the DAVIS dataset, DreamScene4D achieves a mean EPE of 8.56 and median EPE of 4.24 for visible points, and a mean EPE of 6.72 for occluded points.
On the Kubric dataset, DreamScene4D achieves a mean EPE of 14.30 for visible points and 18.31 for occluded points.

Trích dẫn

"DreamScene4D extends video-to-4D generation to multi-object videos with fast motion."
"Our key insight is to design a "decompose-then-recompose" scheme to factorize both the whole video scene and each object's 3D motion."
"DreamScene4D achieves significant improvements compared to the existing SOTA video-to-4D approaches [42, 15] on DAVIS, Kubric [13], and our self-captured videos."

Thông tin chi tiết chính được chắt lọc từ

DreamScene4D: Dynamic Multi-Object Scene Generation from Monocular Videos

by Wen-Hsuan Ch... lúc arxiv.org 05-06-2024

https://arxiv.org/pdf/2405.02280.pdf

Yêu cầu sâu hơn

How can the proposed motion factorization scheme be extended to handle more complex scene dynamics, such as object interactions or articulated motion

The proposed motion factorization scheme in DreamScene4D can be extended to handle more complex scene dynamics by incorporating additional components to account for object interactions or articulated motion. One way to achieve this is by introducing specialized modules that can model the interactions between objects in the scene. For example, a physics-based simulation module could be integrated to simulate realistic object interactions, such as collisions, deformations, or constraints. By incorporating these dynamics into the motion factorization process, the model can better capture the complex interactions between objects in the scene.
Furthermore, for articulated motion, the motion factorization scheme can be enhanced by incorporating hierarchical representations of objects. By decomposing the motion of articulated objects into individual parts or joints, the model can better capture the intricate movements of each component. This hierarchical approach allows for more detailed modeling of articulated motion, enabling the generation of realistic and dynamic scenes with complex object interactions.

What are the potential applications of the generated 4D scenes beyond video perception, such as in graphics, robotics, or virtual environments

The generated 4D scenes from DreamScene4D have a wide range of potential applications beyond video perception. Some of the key applications include:

Graphics: The 4D scenes can be used for creating realistic digital avatars, assets, and environments for video games, movies, and virtual reality experiences. The dynamic nature of the scenes allows for interactive and immersive graphics applications.

Robotics: The 4D scenes can be utilized for robot perception and navigation in dynamic environments. Robots can use the generated scenes to understand and interact with their surroundings, enabling more intelligent and adaptive robotic systems.

Virtual Environments: The 4D scenes can be integrated into virtual environments for training simulations, architectural visualization, and virtual tours. The dynamic nature of the scenes adds realism and depth to virtual experiences, enhancing user engagement and immersion.

Augmented Reality: The 4D scenes can be overlaid onto the real world in augmented reality applications, providing users with interactive and dynamic visualizations of virtual objects and environments in real-time.

By leveraging the capabilities of DreamScene4D to generate realistic and dynamic 4D scenes, these applications can benefit from enhanced visual content and immersive experiences across various domains.

How can the video amodal completion component of DreamScene4D be further improved to handle more challenging occlusion patterns or camera viewpoints

To further improve the video amodal completion component of DreamScene4D for handling more challenging occlusion patterns or camera viewpoints, several enhancements can be considered:

Temporal Consistency: Incorporating additional temporal consistency constraints during the completion process can help maintain coherence across frames, especially in regions with occlusions. By enforcing consistency in object appearances and movements over time, the completion results can be more seamless and realistic.

Semantic Understanding: Introducing semantic segmentation information or object priors can guide the completion process, ensuring that occluded regions are filled with contextually relevant content. By leveraging semantic cues, the model can better infer the content of occluded areas and produce more accurate completions.

Multi-Modal Fusion: Integrating multiple modalities, such as depth information or optical flow, can provide additional cues for completing occluded regions accurately. By fusing information from different sources, the model can better understand the scene geometry and dynamics, leading to improved completion results in challenging scenarios.

By incorporating these enhancements, the video amodal completion component of DreamScene4D can address complex occlusion patterns and camera viewpoints more effectively, resulting in more robust and realistic completion outcomes.