FlowZero: Zero-Shot Text-to-Video Synthesis with LLM-Driven Dynamic Scene Syntax

• URL: http://arxiv.org/abs/2311.15813v1
• Date: Mon, 27 Nov 2023 13:39:44 GMT
• Title: FlowZero: Zero-Shot Text-to-Video Synthesis with LLM-Driven Dynamic Scene Syntax
• Authors: Yu Lu, Linchao Zhu, Hehe Fan, Yi Yang
• Abstract summary: FlowZero is a framework that combines Large Language Models (LLMs) with image diffusion models to generate temporally-herent videos. FlowZero achieves improvement in zero-shot video synthesis, generating coherent videos with vivid motion.
• Score: 72.89879499617858
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-to-video (T2V) generation is a rapidly growing research area that aims to translate the scenes, objects, and actions within complex video text into a sequence of coherent visual frames. We present FlowZero, a novel framework that combines Large Language Models (LLMs) with image diffusion models to generate temporally-coherent videos. FlowZero uses LLMs to understand complex spatio-temporal dynamics from text, where LLMs can generate a comprehensive dynamic scene syntax (DSS) containing scene descriptions, object layouts, and background motion patterns. These elements in DSS are then used to guide the image diffusion model for video generation with smooth object motions and frame-to-frame coherence. Moreover, FlowZero incorporates an iterative self-refinement process, enhancing the alignment between the spatio-temporal layouts and the textual prompts for the videos. To enhance global coherence, we propose enriching the initial noise of each frame with motion dynamics to control the background movement and camera motion adaptively. By using spatio-temporal syntaxes to guide the diffusion process, FlowZero achieves improvement in zero-shot video synthesis, generating coherent videos with vivid motion.

Related papers

• TANGO: Co-Speech Gesture Video Reenactment with Hierarchical Audio Motion Embedding and Diffusion Interpolation [4.019144083959918]
  We present TANGO, a framework for generating co-speech body-gesture videos. Given a few-minute, single-speaker reference video, TANGO produces high-fidelity videos with synchronized body gestures.
  arXiv  Detail & Related papers  (2024-10-05T16:30:46Z)
• LASER: Tuning-Free LLM-Driven Attention Control for Efficient Text-conditioned Image-to-Animation [62.232361821779335]
  We introduce a tuning-free attention control framework, encapsulated by the progressive process of prompt-Aware editing, StablE animation geneRation, abbreviated as LASER. We manipulate the model's spatial features and self-attention mechanisms to maintain animation integrity. Our meticulous control over spatial features and self-attention ensures structural consistency in the images.
  arXiv  Detail & Related papers  (2024-04-21T07:13:56Z)
• Lumiere: A Space-Time Diffusion Model for Video Generation [75.54967294846686]
  We introduce a Space-Time U-Net architecture that generates the entire temporal duration of the video at once. This is in contrast to existing video models which synthesize distants followed by temporal super-resolution. By deploying both spatial and (importantly) temporal down- and up-sampling, our model learns to directly generate a full-frame-rate, low-resolution video.
  arXiv  Detail & Related papers  (2024-01-23T18:05:25Z)
• MoVideo: Motion-Aware Video Generation with Diffusion Models [97.03352319694795]
  We propose a novel motion-aware generation (MoVideo) framework that takes motion into consideration from two aspects: video depth and optical flow. MoVideo achieves state-of-the-art results in both text-to-video and image-to-video generation, showing promising prompt consistency, frame consistency and visual quality.
  arXiv  Detail & Related papers  (2023-11-19T13:36:03Z)
• Free-Bloom: Zero-Shot Text-to-Video Generator with LLM Director and LDM Animator [59.589919015669274]
  This study focuses on zero-shot text-to-video generation considering the data- and cost-efficient. We propose a novel Free-Bloom pipeline that harnesses large language models (LLMs) as the director to generate a semantic-coherence prompt sequence. We also propose a series of annotative modifications to adapting LDMs in the reverse process, including joint noise sampling, step-aware attention shift, and dual-path.
  arXiv  Detail & Related papers  (2023-09-25T19:42:16Z)
• Fine-Grained Spatiotemporal Motion Alignment for Contrastive Video Representation Learning [16.094271750354835]
  Motion information is critical to a robust and generalized video representation. Recent works have adopted frame difference as the source of motion information in video contrastive learning. We present a framework capable of introducing well-aligned and significant motion information.
  arXiv  Detail & Related papers  (2023-09-01T07:03:27Z)
• Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators [70.17041424896507]
  Recent text-to-video generation approaches rely on computationally heavy training and require large-scale video datasets. We propose a new task of zero-shot text-to-video generation using existing text-to-image synthesis methods. Our method performs comparably or sometimes better than recent approaches, despite not being trained on additional video data.
  arXiv  Detail & Related papers  (2023-03-23T17:01:59Z)

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.