Compositional 3D-aware Video Generation with LLM Director

• URL: http://arxiv.org/abs/2409.00558v1
• Date: Sat, 31 Aug 2024 23:07:22 GMT
• Title: Compositional 3D-aware Video Generation with LLM Director
• Authors: Hanxin Zhu, Tianyu He, Anni Tang, Junliang Guo, Zhibo Chen, Jiang Bian,
• Abstract summary: We propose a novel paradigm that generates each concept in 3D representation separately and then composes them with priors from Large Language Models and 2D diffusion models. Our method can generate high-fidelity videos from text with diverse motion and flexible control over each concept.
• Score: 27.61057927559143
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Significant progress has been made in text-to-video generation through the use of powerful generative models and large-scale internet data. However, substantial challenges remain in precisely controlling individual concepts within the generated video, such as the motion and appearance of specific characters and the movement of viewpoints. In this work, we propose a novel paradigm that generates each concept in 3D representation separately and then composes them with priors from Large Language Models (LLM) and 2D diffusion models. Specifically, given an input textual prompt, our scheme consists of three stages: 1) We leverage LLM as the director to first decompose the complex query into several sub-prompts that indicate individual concepts within the video~(\textit{e.g.}, scene, objects, motions), then we let LLM to invoke pre-trained expert models to obtain corresponding 3D representations of concepts. 2) To compose these representations, we prompt multi-modal LLM to produce coarse guidance on the scales and coordinates of trajectories for the objects. 3) To make the generated frames adhere to natural image distribution, we further leverage 2D diffusion priors and use Score Distillation Sampling to refine the composition. Extensive experiments demonstrate that our method can generate high-fidelity videos from text with diverse motion and flexible control over each concept. Project page: \url{https://aka.ms/c3v}.

Related papers

• VideoGLaMM: A Large Multimodal Model for Pixel-Level Visual Grounding in Videos [58.765796160750504]
  VideoGLaMM is a new model for fine-grained pixel-level grounding in videos based on user-provided textual inputs. The architecture is trained to synchronize both spatial and temporal elements of video content with textual instructions. Experimental results show that our model consistently outperforms existing approaches across all three tasks.
  arXiv  Detail & Related papers  (2024-11-07T17:59:27Z)
• Story3D-Agent: Exploring 3D Storytelling Visualization with Large Language Models [57.30913211264333]
  We present Story3D-Agent, a pioneering approach that transforms provided narratives into 3D-rendered visualizations. By integrating procedural modeling, our approach enables precise control over multi-character actions and motions, as well as diverse decorative elements. We have thoroughly evaluated our Story3D-Agent to validate its effectiveness, offering a basic framework to advance 3D story representation.
  arXiv  Detail & Related papers  (2024-08-21T17:43:15Z)
• VIMI: Grounding Video Generation through Multi-modal Instruction [89.90065445082442]
  Existing text-to-video diffusion models rely solely on text-only encoders for their pretraining. We construct a large-scale multimodal prompt dataset by employing retrieval methods to pair in-context examples with the given text prompts. We finetune the model from the first stage on three video generation tasks, incorporating multi-modal instructions.
  arXiv  Detail & Related papers  (2024-07-08T18:12:49Z)
• Vivid-ZOO: Multi-View Video Generation with Diffusion Model [76.96449336578286]
  New challenges lie in the lack of massive captioned multi-view videos and the complexity of modeling such multi-dimensional distribution. We propose a novel diffusion-based pipeline that generates high-quality multi-view videos centered around a dynamic 3D object from text.
  arXiv  Detail & Related papers  (2024-06-12T21:44:04Z)
• VideoMV: Consistent Multi-View Generation Based on Large Video Generative Model [34.35449902855767]
  Two fundamental questions are what data we use for training and how to ensure multi-view consistency. We propose a dense consistent multi-view generation model that is fine-tuned from off-the-shelf video generative models. Our approach can generate 24 dense views and converges much faster in training than state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-03-18T17:48:15Z)
• Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization [52.63845811751936]
  Video pre-training is challenging due to the modeling of its dynamics video. In this paper, we address such limitations in video pre-training with an efficient video decomposition. Our framework is both capable of comprehending and generating image and video content, as demonstrated by its performance across 13 multimodal benchmarks.
  arXiv  Detail & Related papers  (2024-02-05T16:30:49Z)
• LLM-grounded Video Diffusion Models [57.23066793349706]
  Video diffusion models have emerged as a promising tool for neuraltemporal generation. Current models struggle with prompts and often restricted or incorrect motion. We introduce LLM-grounded Video Diffusion (LVD) Our results demonstrate that LVD significantly outperforms its base video diffusion model.
  arXiv  Detail & Related papers  (2023-09-29T17:54:46Z)

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.