Fast Dynamic 3D Object Generation from a Single-view Video
(2401.08742)Abstract
Generating dynamic 3D object from a single-view video is challenging due to the lack of 4D labeled data. Extending image-to-3D pipelines by transferring off-the-shelf image generation models such as score distillation sampling, existing methods tend to be slow and expensive to scale due to the need for back-propagating the information-limited supervision signals through a large pretrained model. To address this, we propose an efficient video-to-4D object generation framework called Efficient4D. It generates high-quality spacetime-consistent images under different camera views, and then uses them as labeled data to directly train a novel 4D Gaussian splatting model with explicit point cloud geometry, enabling real-time rendering under continuous camera trajectories. Extensive experiments on synthetic and real videos show that Efficient4D offers a remarkable 20-fold increase in speed when compared to prior art alternatives while preserving the quality of novel view synthesis. For example, Efficient4D takes only 6 mins to model a dynamic object, vs 120 mins by Consistent4D.
Overview
-
The research introduces Efficient4D, a framework for fast dynamic 3D object creation from single-view videos.
-
Efficient4D allows real-time rendering and produces consistent high-quality images across different angles and moments.
-
The approach uses a two-stage pipeline, including synthetic training data generation and a novel 4D Gaussian splatting model.
-
Efficient4D is ten times faster than existing methods, taking about 14 minutes to model a dynamic object.
-
It shows promise for practical applications in gaming, VR, and film, with potential for future improvements in handling long videos.
Overview
Researchers have developed an innovative framework named Efficient4D, which significantly expedites the process of creating dynamic 3D objects from single-view videos. This advancement allows real-time rendering under varying camera trajectories and generates high-quality images that are consistent in both space and time.
The Challenge
Traditional methods struggle with dynamic 3D object generation, requiring an extensive amount of time and resources due to the need for heavy supervision and the use of large pre-trained models. These methods take approximately 150 minutes per object, making them impractical for scaling up to larger datasets or more complex objects.
The Solution: Efficient4D
The newly proposed Efficient4D addresses these limitations by introducing a two-stage pipeline. The first stage involves generating a matrix of spatially and temporally consistent images from different camera views. These images serve as synthetic training data, which then directly inform the training of a novel 4D Gaussian splatting model. This model incorporates explicit point cloud geometry and is optimized for real-time rendering. By utilizing a Gaussian representation, the framework achieves further computational efficiency compared to NeRF-based designs.
Performance and Findings
Extensive experiments using both synthetic and real videos demonstrate that Efficient4D delivers a tenfold increase in speed compared to previous methods while maintaining the same level of view synthesis quality. Astonishingly, Efficient4D is capable of modeling a dynamic object in just 14 minutes. It also performs well in few-shot scenarios, needing only a minimal number of keyframes, thereby broadening the practical applications of video-to-4D object generation. The utilization of a confidence-aware loss function in training enhances the resilience of the model to inconsistencies in the generated training data.
Concluding Remarks
Efficient4D stands as a significant leap forward in the field of dynamic 3D object generation, making it feasible to produce high-quality 4D renderings in real time. This breakthrough opens the door to numerous applications that require rapid and accurate 3D modeling, such as video games, virtual reality, and film production. The method's limitations regarding long-duration video handling hint at potential areas for future development, possibly involving global receptive fields or scalable data handling techniques.
Create an account to read this summary for free: