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L4GM: Large 4D Gaussian Reconstruction Model (2406.10324v1)

Published 14 Jun 2024 in cs.CV and cs.LG

Abstract: We present L4GM, the first 4D Large Reconstruction Model that produces animated objects from a single-view video input -- in a single feed-forward pass that takes only a second. Key to our success is a novel dataset of multiview videos containing curated, rendered animated objects from Objaverse. This dataset depicts 44K diverse objects with 110K animations rendered in 48 viewpoints, resulting in 12M videos with a total of 300M frames. We keep our L4GM simple for scalability and build directly on top of LGM, a pretrained 3D Large Reconstruction Model that outputs 3D Gaussian ellipsoids from multiview image input. L4GM outputs a per-frame 3D Gaussian Splatting representation from video frames sampled at a low fps and then upsamples the representation to a higher fps to achieve temporal smoothness. We add temporal self-attention layers to the base LGM to help it learn consistency across time, and utilize a per-timestep multiview rendering loss to train the model. The representation is upsampled to a higher framerate by training an interpolation model which produces intermediate 3D Gaussian representations. We showcase that L4GM that is only trained on synthetic data generalizes extremely well on in-the-wild videos, producing high quality animated 3D assets.

Citations (10)

Summary

  • The paper introduces L4GM, a breakthrough model that rapidly reconstructs animated 3D objects from single-view video input.
  • The model leverages a 12 million-video dataset and a per-timestep multiview loss to achieve state-of-the-art quality with 100- to 1,000-fold speed improvements.
  • The methodology’s novel upsampling technique produces temporally smooth 4D reconstructions, paving the way for applications in VR, AR, and digital content creation.

L4GM: Large 4D Gaussian Reconstruction Model

The paper introduces L4GM, the first large reconstruction model for 4D data designed to generate animated 3D objects from single-view video input within a remarkably short processing time. The pivotal contribution of this work is the development of a novel model that can handle the complexities of dynamic scenes and a significant dataset that supports the effective training of such a model.

Key Contributions

  1. Novel Dataset: The researchers assembled a novel dataset comprising 12 million multiview videos using rendered animated objects from Objaverse. This dataset captures 44,000 diverse objects and 110,000 animations across 48 viewpoints, totaling 300 million frames. This extensive dataset is instrumental in training the L4GM model to generalize effectively from synthetic to real-world video data.
  2. Model Architecture: L4GM is built on LGM, a pretrained 3D Large Reconstruction Model that outputs 3D Gaussian ellipsoids from multiview image inputs. L4GM extends this base model to process temporal data by incorporating temporal self-attention layers. This enhancement enables the model to maintain temporal consistency across frames while handling the intricacies of dynamic 3D object reconstruction.
  3. Upsampling Technique: The model employs an upsampling process where it outputs per-frame 3D Gaussian Splatting representations from low-fps video frames and upscales these representations to achieve higher temporal resolutions. This technique ensures that the final animated 3D objects exhibit temporal smoothness.
  4. Training and Inference: The training leverages a per-timestep multiview rendering loss to refine the model's accuracy. During inference, L4GM showcases an impressive ability to generalize from synthetic training data to real-world videos.

Strong Numerical Results and Claims

The paper reports significant advancements in the domain with notable numerical results:

  • L4GM achieves state-of-the-art quality in video-to-4D benchmarks.
  • The model operates at a speed 100 to 1,000 times faster than existing methodologies, marking a substantial improvement in efficiency.
  • On the Consistent4D benchmark, L4GM's reconstruction quality metrics (LPIPS, CLIP similarity, and FVD) outperform those of leading approaches.

Implications

Practical Implications: The ability of L4GM to generate high-quality animated 3D objects from single-view video input rapidly opens up numerous applications in the fields of virtual reality, augmented reality, gaming, and digital content creation. The model's generalization capabilities suggest that creators can leverage widely available monocular video footage to produce detailed 4D representations without the need for extensive multiview footage.

Theoretical Implications: The architectural advancements in L4GM, particularly the integration of temporal self-attention layers, present a new direction for future research in dynamic scene reconstruction. The scalability and efficiency demonstrated by L4GM provide a foundation for developing more complex models capable of handling even richer datasets and more intricate temporal dynamics.

Speculation on Future Developments

The future trajectory of research in this domain might see:

  • Enhanced Dataset Diversity: Expanding datasets to include more varied and realistic animations could further improve the model's generalization capabilities.
  • Human-In-The-Loop: Future models may incorporate human-in-the-loop methodologies for fine-tuning and editing 4D content, thus integrating automated processes with expert intervention for superior quality outputs.
  • Real-Time Applications: With continued advancements, real-time applications such as live video editing and interactive virtual environments seem feasible.
  • Integration with Other Modalities: Combining 4D reconstruction with other sensory data like depth sensors and motion capture could yield even more robust and versatile models.

The development of L4GM marks a significant step in the evolution of dynamic scene reconstruction, showcasing how large-scale data and advanced model architectures can converge to produce practical and theoretically insightful advancements in computer vision and graphics. This paper provides a comprehensive foundation for future innovations in automated 4D content creation, promising considerable impacts across multiple industries and areas of research.