Motion Control for Enhanced Complex Action Video Generation
- URL: http://arxiv.org/abs/2411.08328v1
- Date: Wed, 13 Nov 2024 04:20:45 GMT
- Title: Motion Control for Enhanced Complex Action Video Generation
- Authors: Qiang Zhou, Shaofeng Zhang, Nianzu Yang, Ye Qian, Hao Li,
- Abstract summary: Existing text-to-video (T2V) models often struggle with generating videos with sufficiently pronounced or complex actions.
We propose a novel framework, MVideo, designed to produce long-duration videos with precise, fluid actions.
MVideo overcomes the limitations of text prompts by incorporating mask sequences as an additional motion condition input.
- Score: 17.98485830881648
- License:
- Abstract: Existing text-to-video (T2V) models often struggle with generating videos with sufficiently pronounced or complex actions. A key limitation lies in the text prompt's inability to precisely convey intricate motion details. To address this, we propose a novel framework, MVideo, designed to produce long-duration videos with precise, fluid actions. MVideo overcomes the limitations of text prompts by incorporating mask sequences as an additional motion condition input, providing a clearer, more accurate representation of intended actions. Leveraging foundational vision models such as GroundingDINO and SAM2, MVideo automatically generates mask sequences, enhancing both efficiency and robustness. Our results demonstrate that, after training, MVideo effectively aligns text prompts with motion conditions to produce videos that simultaneously meet both criteria. This dual control mechanism allows for more dynamic video generation by enabling alterations to either the text prompt or motion condition independently, or both in tandem. Furthermore, MVideo supports motion condition editing and composition, facilitating the generation of videos with more complex actions. MVideo thus advances T2V motion generation, setting a strong benchmark for improved action depiction in current video diffusion models. Our project page is available at https://mvideo-v1.github.io/.
Related papers
- Enhancing Motion in Text-to-Video Generation with Decomposed Encoding and Conditioning [26.44634685830323]
We propose a novel framework called DEcomposed MOtion (DEMO) to enhance motion synthesis in Text-to-Video (T2V) generation.
Our method includes a content encoder for static elements and a motion encoder for temporal dynamics, alongside separate content and motion conditioning mechanisms.
We demonstrate DEMO's superior ability to produce videos with enhanced motion dynamics while maintaining high visual quality.
arXiv Detail & Related papers (2024-10-31T17:59:53Z) - RACCooN: A Versatile Instructional Video Editing Framework with Auto-Generated Narratives [58.15403987979496]
This paper proposes RACCooN, a versatile and user-friendly video-to-paragraph-to-video generative framework.
Our video generative model incorporates auto-generated narratives or instructions to enhance the quality and accuracy of the generated content.
The proposed framework demonstrates impressive versatile capabilities in video-to-paragraph generation, video content editing, and can be incorporated into other SoTA video generative models for further enhancement.
arXiv Detail & Related papers (2024-05-28T17:46:36Z) - DreamVideo: Composing Your Dream Videos with Customized Subject and
Motion [52.7394517692186]
We present DreamVideo, a novel approach to generating personalized videos from a few static images of the desired subject.
DreamVideo decouples this task into two stages, subject learning and motion learning, by leveraging a pre-trained video diffusion model.
In motion learning, we architect a motion adapter and fine-tune it on the given videos to effectively model the target motion pattern.
arXiv Detail & Related papers (2023-12-07T16:57:26Z) - LivePhoto: Real Image Animation with Text-guided Motion Control [51.31418077586208]
This work presents a practical system, named LivePhoto, which allows users to animate an image of their interest with text descriptions.
We first establish a strong baseline that helps a well-learned text-to-image generator (i.e., Stable Diffusion) take an image as a further input.
We then equip the improved generator with a motion module for temporal modeling and propose a carefully designed training pipeline to better link texts and motions.
arXiv Detail & Related papers (2023-12-05T17:59:52Z) - VMC: Video Motion Customization using Temporal Attention Adaption for
Text-to-Video Diffusion Models [58.93124686141781]
Video Motion Customization (VMC) is a novel one-shot tuning approach crafted to adapt temporal attention layers within video diffusion models.
Our approach introduces a novel motion distillation objective using residual vectors between consecutive frames as a motion reference.
We validate our method against state-of-the-art video generative models across diverse real-world motions and contexts.
arXiv Detail & Related papers (2023-12-01T06:50:11Z) - ConditionVideo: Training-Free Condition-Guided Text-to-Video Generation [33.37279673304]
We introduce ConditionVideo, a training-free approach to text-to-video generation based on the provided condition, video, and input text.
ConditionVideo generates realistic dynamic videos from random noise or given scene videos.
Our method exhibits superior performance in terms of frame consistency, clip score, and conditional accuracy, outperforming other compared methods.
arXiv Detail & Related papers (2023-10-11T17:46:28Z) - Control-A-Video: Controllable Text-to-Video Diffusion Models with Motion Prior and Reward Feedback Learning [50.60891619269651]
Control-A-Video is a controllable T2V diffusion model that can generate videos conditioned on text prompts and reference control maps like edge and depth maps.
We propose novel strategies to incorporate content prior and motion prior into the diffusion-based generation process.
Our framework generates higher-quality, more consistent videos compared to existing state-of-the-art methods in controllable text-to-video generation.
arXiv Detail & Related papers (2023-05-23T09:03:19Z) - LaMD: Latent Motion Diffusion for Video Generation [69.4111397077229]
latent motion diffusion (LaMD) framework consists of a motion-decomposed video autoencoder and a diffusion-based motion generator.
Results show that LaMD generates high-quality videos with a wide range of motions, from dynamics to highly controllable movements.
arXiv Detail & Related papers (2023-04-23T10:32:32Z)
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.