MotionCraft: Physics-based Zero-Shot Video Generation

• URL: http://arxiv.org/abs/2405.13557v2
• Date: Fri, 25 Oct 2024 10:01:22 GMT
• Title: MotionCraft: Physics-based Zero-Shot Video Generation
• Authors: Luca Savant Aira, Antonio Montanaro, Emanuele Aiello, Diego Valsesia, Enrico Magli,
• Abstract summary: MotionCraft is a new zero-shot video generator to craft physics-based and realistic videos. We show that MotionCraft is able to warp the noise latent space of an image diffusion model, such as Stable Diffusion, by applying an optical flow. We compare our method with the state-of-the-art Text2Video-Zero reporting qualitative and quantitative improvements.
• Score: 22.33113030344355
• License:
• Abstract: Generating videos with realistic and physically plausible motion is one of the main recent challenges in computer vision. While diffusion models are achieving compelling results in image generation, video diffusion models are limited by heavy training and huge models, resulting in videos that are still biased to the training dataset. In this work we propose MotionCraft, a new zero-shot video generator to craft physics-based and realistic videos. MotionCraft is able to warp the noise latent space of an image diffusion model, such as Stable Diffusion, by applying an optical flow derived from a physics simulation. We show that warping the noise latent space results in coherent application of the desired motion while allowing the model to generate missing elements consistent with the scene evolution, which would otherwise result in artefacts or missing content if the flow was applied in the pixel space. We compare our method with the state-of-the-art Text2Video-Zero reporting qualitative and quantitative improvements, demonstrating the effectiveness of our approach to generate videos with finely-prescribed complex motion dynamics. Project page: https://mezzelfo.github.io/MotionCraft/

Related papers

• PhysGen: Rigid-Body Physics-Grounded Image-to-Video Generation [29.831214435147583]
  We present PhysGen, a novel image-to-video generation method. It produces a realistic, physically plausible, and temporally consistent video. Our key insight is to integrate model-based physical simulation with a data-driven video generation process.
  arXiv  Detail & Related papers  (2024-09-27T17:59:57Z)
• VideoPhy: Evaluating Physical Commonsense for Video Generation [93.28748850301949]
  We present VideoPhy, a benchmark designed to assess whether the generated videos follow physical commonsense for real-world activities. We then generate videos conditioned on captions from diverse state-of-the-art text-to-video generative models. Our human evaluation reveals that the existing models severely lack the ability to generate videos adhering to the given text prompts.
  arXiv  Detail & Related papers  (2024-06-05T17:53:55Z)
• DreamPhysics: Learning Physical Properties of Dynamic 3D Gaussians with Video Diffusion Priors [75.83647027123119]
  We propose to learn the physical properties of a material field with video diffusion priors. We then utilize a physics-based Material-Point-Method simulator to generate 4D content with realistic motions.
  arXiv  Detail & Related papers  (2024-06-03T16:05:25Z)
• Controllable Longer Image Animation with Diffusion Models [12.565739255499594]
  We introduce an open-domain controllable image animation method using motion priors with video diffusion models. Our method achieves precise control over the direction and speed of motion in the movable region by extracting the motion field information from videos. We propose an efficient long-duration video generation method based on noise reschedule specifically tailored for image animation tasks.
  arXiv  Detail & Related papers  (2024-05-27T16:08:00Z)
• MotionCrafter: One-Shot Motion Customization of Diffusion Models [66.44642854791807]
  We introduce MotionCrafter, a one-shot instance-guided motion customization method. MotionCrafter employs a parallel spatial-temporal architecture that injects the reference motion into the temporal component of the base model. During training, a frozen base model provides appearance normalization, effectively separating appearance from motion.
  arXiv  Detail & Related papers  (2023-12-08T16:31:04Z)
• 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)
• 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)
• PhysDiff: Physics-Guided Human Motion Diffusion Model [101.1823574561535]
  Existing motion diffusion models largely disregard the laws of physics in the diffusion process. PhysDiff incorporates physical constraints into the diffusion process. Our approach achieves state-of-the-art motion quality and improves physical plausibility drastically.
  arXiv  Detail & Related papers  (2022-12-05T18:59:52Z)

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.