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Efficient4D: Fast Dynamic 3D Object Generation from a Single-view Video (2401.08742v3)

Published 16 Jan 2024 in cs.CV

Abstract: Generating dynamic 3D object from a single-view video is challenging due to the lack of 4D labeled data. An intuitive approach is to extend previous image-to-3D pipelines by transferring off-the-shelf image generation models such as score distillation sampling.However, this approach would 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 reconstruct the 4D content through a 4D Gaussian splatting model. Importantly, our method can achieve real-time rendering under continuous camera trajectories. To enable robust reconstruction under sparse views, we introduce inconsistency-aware confidence-weighted loss design, along with a lightly weighted score distillation loss. Extensive experiments on both synthetic and real videos show that Efficient4D offers a remarkable 10-fold increase in speed when compared to prior art alternatives while preserving the quality of novel view synthesis. For example, Efficient4D takes only 10 minutes to model a dynamic object, vs 120 minutes by the previous art model Consistent4D.

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Citations (23)
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Summary

  • The paper presents a two-stage pipeline that reduces dynamic 3D object generation from 150 minutes to just 14 minutes using a novel 4D Gaussian splatting model.
  • The method leverages spatially and temporally consistent synthetic training images and point cloud geometry to achieve real-time rendering.
  • Efficient4D incorporates a confidence-aware loss function and few-shot training, broadening its use in applications like video games, VR, and film production.

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.

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