MotionDreamer: Exploring Semantic Video Diffusion features for Zero-Shot 3D Mesh Animation

• URL: http://arxiv.org/abs/2405.20155v2
• Date: Thu, 14 Nov 2024 13:07:26 GMT
• Title: MotionDreamer: Exploring Semantic Video Diffusion features for Zero-Shot 3D Mesh Animation
• Authors: Lukas Uzolas, Elmar Eisemann, Petr Kellnhofer,
• Abstract summary: We propose a technique for automatic re-animation of various 3D shapes based on a motion prior extracted from a video diffusion model. We leverage an explicit mesh-based representation compatible with existing computer-graphics pipelines. Our time-efficient zero-shot method achieves a superior performance re-animating a diverse set of 3D shapes.
• Score: 10.263762787854862
• License:
• Abstract: Animation techniques bring digital 3D worlds and characters to life. However, manual animation is tedious and automated techniques are often specialized to narrow shape classes. In our work, we propose a technique for automatic re-animation of various 3D shapes based on a motion prior extracted from a video diffusion model. Unlike existing 4D generation methods, we focus solely on the motion, and we leverage an explicit mesh-based representation compatible with existing computer-graphics pipelines. Furthermore, our utilization of diffusion features enhances accuracy of our motion fitting. We analyze efficacy of these features for animation fitting and we experimentally validate our approach for two different diffusion models and four animation models. Finally, we demonstrate that our time-efficient zero-shot method achieves a superior performance re-animating a diverse set of 3D shapes when compared to existing techniques in a user study. The project website is located at https://lukas.uzolas.com/MotionDreamer.

Related papers

• Tex4D: Zero-shot 4D Scene Texturing with Video Diffusion Models [54.35214051961381]
  3D meshes are widely used in computer vision and graphics for their efficiency in animation and minimal memory use in movies, games, AR, and VR. However, creating temporal consistent and realistic textures for mesh remains labor-intensive for professional artists. We present 3D Tex sequences that integrates inherent geometry from mesh sequences with video diffusion models to produce consistent textures.
  arXiv  Detail & Related papers  (2024-10-14T17:59:59Z)
• 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)
• iHuman: Instant Animatable Digital Humans From Monocular Videos [16.98924995658091]
  We present a fast, simple, yet effective method for creating animatable 3D digital humans from monocular videos. This work achieves and illustrates the need of accurate 3D mesh-type modelling of the human body. Our method is faster by an order of magnitude (in terms of training time) than its closest competitor.
  arXiv  Detail & Related papers  (2024-07-15T18:51:51Z)
• 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)
• Animate124: Animating One Image to 4D Dynamic Scene [108.17635645216214]
  Animate124 is the first work to animate a single in-the-wild image into 3D video through textual motion descriptions. Our method demonstrates significant advancements over existing baselines.
  arXiv  Detail & Related papers  (2023-11-24T16:47:05Z)
• MAS: Multi-view Ancestral Sampling for 3D motion generation using 2D diffusion [57.90404618420159]
  We introduce Multi-view Ancestral Sampling (MAS), a method for 3D motion generation. MAS works by simultaneously denoising multiple 2D motion sequences representing different views of the same 3D motion. We demonstrate MAS on 2D pose data acquired from videos depicting professional basketball maneuvers.
  arXiv  Detail & Related papers  (2023-10-23T09:05:18Z)
• Versatile Face Animator: Driving Arbitrary 3D Facial Avatar in RGBD Space [38.940128217895115]
  We propose Versatile Face Animator, which combines facial motion capture with motion in an end-to-end manner, eliminating the need for blendshapes or rigs. Our method has the following two main characteristics: 1) we propose an RGBD animation module to learn facial motion from raw RGBD videos by hierarchical motion dictionaries and animate RGBD images rendered from 3D facial mesh coarse-to-fine, enabling facial animation on arbitrary 3D characters. Comprehensive experiments demonstrate the effectiveness of our proposed framework in generating impressive 3D facial animation results.
  arXiv  Detail & Related papers  (2023-08-11T11:29:01Z)
• ARTIC3D: Learning Robust Articulated 3D Shapes from Noisy Web Image Collections [71.46546520120162]
  Estimating 3D articulated shapes like animal bodies from monocular images is inherently challenging. We propose ARTIC3D, a self-supervised framework to reconstruct per-instance 3D shapes from a sparse image collection in-the-wild. We produce realistic animations by fine-tuning the rendered shape and texture under rigid part transformations.
  arXiv  Detail & Related papers  (2023-06-07T17:47:50Z)
• Going beyond Free Viewpoint: Creating Animatable Volumetric Video of Human Performances [7.7824496657259665]
  We present an end-to-end pipeline for the creation of high-quality animatable volumetric video content of human performances. Semantic enrichment and geometric animation ability are achieved by establishing temporal consistency in the 3D data. For pose editing, we exploit the captured data as much as possible and kinematically deform the captured frames to fit a desired pose.
  arXiv  Detail & Related papers  (2020-09-02T09:46:12Z)

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.