L4GM: Large 4D Gaussian Reconstruction Model

• URL: http://arxiv.org/abs/2406.10324v1
• Date: Fri, 14 Jun 2024 17:51:18 GMT
• Title: L4GM: Large 4D Gaussian Reconstruction Model
• Authors: Jiawei Ren, Kevin Xie, Ashkan Mirzaei, Hanxue Liang, Xiaohui Zeng, Karsten Kreis, Ziwei Liu, Antonio Torralba, Sanja Fidler, Seung Wook Kim, Huan Ling,
• Abstract summary: We present L4GM, the first 4D Large Reconstruction Model that produces animated objects from a single-view video input. Key to our success is a novel dataset of multiview videos containing curated, rendered animated objects.
• Score: 99.82220378522624
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Animate3D: Animating Any 3D Model with Multi-view Video Diffusion [47.05131487114018]
  Animate3D is a novel framework for animating any static 3D model. We introduce a framework combining reconstruction and 4D Score Distillation Sampling (4D-SDS) to leverage the multi-view video diffusion priors for animating 3D objects.
  arXiv  Detail & Related papers  (2024-07-16T05:35:57Z)
• MVGamba: Unify 3D Content Generation as State Space Sequence Modeling [150.80564081817786]
  We introduce MVGamba, a general and lightweight Gaussian reconstruction model featuring a multi-view Gaussian reconstructor. With off-the-detail multi-view diffusion models integrated, MVGamba unifies 3D generation tasks from a single image, sparse images, or text prompts. Experiments demonstrate that MVGamba outperforms state-of-the-art baselines in all 3D content generation scenarios with approximately only $0.1times$ of the model size.
  arXiv  Detail & Related papers  (2024-06-10T15:26:48Z)
• SuperGaussian: Repurposing Video Models for 3D Super Resolution [67.19266415499139]
  We present a simple, modular, and generic method that upsamples coarse 3D models by adding geometric and appearance details. We demonstrate that it is possible to directly repurpose existing (pretrained) video models for 3D super-resolution.
  arXiv  Detail & Related papers  (2024-06-02T03:44:50Z)
• GS-LRM: Large Reconstruction Model for 3D Gaussian Splatting [49.32327147931905]
  We propose GS-LRM, a scalable large reconstruction model that can predict high-quality 3D Gaussians from 2-4 posed sparse images in 0.23 seconds on single A100 GPU. Our model features a very simple transformer-based architecture; we patchify input posed images, pass the primitive multi-view image tokens through a sequence of transformer blocks, and decode final per-pixel Gaussian parameters directly from these tokens for differentiable rendering.
  arXiv  Detail & Related papers  (2024-04-30T16:47:46Z)
• LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation [51.19871052619077]
  We introduce Large Multi-View Gaussian Model (LGM), a novel framework designed to generate high-resolution 3D models from text prompts or single-view images. We maintain the fast speed to generate 3D objects within 5 seconds while boosting the training resolution to 512, thereby achieving high-resolution 3D content generation.
  arXiv  Detail & Related papers  (2024-02-07T17:57:03Z)
• Efficient4D: Fast Dynamic 3D Object Generation from a Single-view Video [42.10482273572879]
  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. Experiments on both synthetic and real videos show that Efficient4D offers a remarkable 10-fold increase in speed.
  arXiv  Detail & Related papers  (2024-01-16T18:58:36Z)
• LRM: Large Reconstruction Model for Single Image to 3D [61.47357798633123]
  We propose the first Large Reconstruction Model (LRM) that predicts the 3D model of an object from a single input image within just 5 seconds. LRM adopts a highly scalable transformer-based architecture with 500 million learnable parameters to directly predict a neural radiance field (NeRF) from the input image. We train our model in an end-to-end manner on massive multi-view data containing around 1 million objects.
  arXiv  Detail & Related papers  (2023-11-08T00:03:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.