DreamScene4D: Dynamic Multi-Object Scene Generation from Monocular Videos
(2405.02280)Abstract
Existing VLMs can track in-the-wild 2D video objects while current generative models provide powerful visual priors for synthesizing novel views for the highly under-constrained 2D-to-3D object lifting. Building upon this exciting progress, we present DreamScene4D, the first approach that can generate three-dimensional dynamic scenes of multiple objects from monocular in-the-wild videos with large object motion across occlusions and novel viewpoints. Our key insight is to design a "decompose-then-recompose" scheme to factorize both the whole video scene and each object's 3D motion. We first decompose the video scene by using open-vocabulary mask trackers and an adapted image diffusion model to segment, track, and amodally complete the objects and background in the video. Each object track is mapped to a set of 3D Gaussians that deform and move in space and time. We also factorize the observed motion into multiple components to handle fast motion. The camera motion can be inferred by re-rendering the background to match the video frames. For the object motion, we first model the object-centric deformation of the objects by leveraging rendering losses and multi-view generative priors in an object-centric frame, then optimize object-centric to world-frame transformations by comparing the rendered outputs against the perceived pixel and optical flow. Finally, we recompose the background and objects and optimize for relative object scales using monocular depth prediction guidance. We show extensive results on the challenging DAVIS, Kubric, and self-captured videos, detail some limitations, and provide future directions. Besides 4D scene generation, our results show that DreamScene4D enables accurate 2D point motion tracking by projecting the inferred 3D trajectories to 2D, while never explicitly trained to do so.
Overview
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DreamScene4D innovates in transforming 2D video into dynamic 3D scenes, addressing the challenge for applications in VR, AR, and more.
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The decompose-then-recompose technique breaks down videos into object and background elements, generating detailed 3D scenes that handle complex movements.
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The technology has outperformed current methods in tests and has potential for enhanced VR/AR experiences, autonomous systems, and the creative industry.
Unpacking "DreamScene4D": 3D Scene Generation from Monocular Videos
Introduction
In the quest to enhance our digital interaction with the physical world, one challenge has been converting 2D video data into three-dimensional and time-resolved (4D) scene models. This capability could fundamentally alter fields like VR, AR, autonomous driving, and more by providing richer interaction layers between real and virtual environments. DreamScene4D introduces a novel approach to synthesize dynamic 3D scenes from monocular videos featuring multiple objects and movements—a task that previously faced significant limitations.
Approach Overview
- Decompose-Then-Recompose Scheme: The technique introduced by DreamScene4D innovatively decomposes videos into object and background components, handles occlusions, and reconstructs the scene in 3D. This method ensures detailed and dynamic 3D scene generation.
- Video Scene Decomposition: Initial segmentation and tracking of each object in a video are performed, handling occlusions by using advanced inpainting techniques that fill in obscured sections of the video.
- 3D Object and Scene Reconstruction: After decomposition, each object and the background are reconstructed in 3D. The model makes use of Gaussian representations for objects, which are optimized for both static and dynamic forms.
- Motion Modeling: The motion of objects is deconstructed into simpler forms to separately handle camera motion, object deformation, and scaling transformations. This separation allows for more accurate and robust handling of complex movements and interactions within the video.
Key Findings and Results
- DreamScene4D was extensively tested on challenging datasets such as DAVIS and Kubric, showing superior performance in generating dynamic 3D scenes from complex videos compared to existing state-of-the-art methods. This includes handling videos with rapid movement, occlusions, and multiple objects.
- An interesting by-product of the method is its capability to accurately track 2D points on objects through their 3D trajectory projections, despite not being explicitly trained for this task.
Practical Implications and Future Prospects
While DreamScene4D has shown impressive capabilities, its real-world applicability can be extended further:
- Enhanced VR and AR Experiences: By improving how dynamic real-world scenes are converted into 3D models, DreamScene4D can lead to much more immersive and interactive VR and AR applications.
- Robotics and Autonomous Systems: For systems that interact with dynamic environments, this technology could provide better contextual understanding and safer navigation strategies.
- Creative and Entertainment Industries: In filmmaking and game development, the ability to convert regular video into detailed 3D models can revolutionize how digital assets are created.
Looking ahead, the continued refinement of 3D scene reconstruction techniques will likely focus on improving the handling of even more complex scene dynamics, more efficient processing methods, and better integration with real-time applications.
Conclusion
DreamScene4D marks a significant step forward in the video-to-3D scene generation domain, particularly for complex scenarios with multiple interacting objects and considerable movements. Its "decompose-then-recompose" strategy effectively addresses previous limitations, setting the stage for groundbreaking applications across various fields. The future developments in this area are poised to enhance our interaction with and understanding of both digital and physical environments.
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