AnimateZoo: Zero-shot Video Generation of Cross-Species Animation via Subject Alignment

• URL: http://arxiv.org/abs/2404.04946v1
• Date: Sun, 7 Apr 2024 12:57:41 GMT
• Title: AnimateZoo: Zero-shot Video Generation of Cross-Species Animation via Subject Alignment
• Authors: Yuanfeng Xu, Yuhao Chen, Zhongzhan Huang, Zijian He, Guangrun Wang, Philip Torr, Liang Lin,
• Abstract summary: We present AnimateZoo, a zero-shot diffusion-based video generator to produce animal animations. Key technique used in our AnimateZoo is subject alignment, which includes two steps. Our model is capable of generating videos characterized by accurate movements, consistent appearance, and high-fidelity frames.
• Score: 64.02822911038848
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent video editing advancements rely on accurate pose sequences to animate subjects. However, these efforts are not suitable for cross-species animation due to pose misalignment between species (for example, the poses of a cat differs greatly from that of a pig due to differences in body structure). In this paper, we present AnimateZoo, a zero-shot diffusion-based video generator to address this challenging cross-species animation issue, aiming to accurately produce animal animations while preserving the background. The key technique used in our AnimateZoo is subject alignment, which includes two steps. First, we improve appearance feature extraction by integrating a Laplacian detail booster and a prompt-tuning identity extractor. These components are specifically designed to capture essential appearance information, including identity and fine details. Second, we align shape features and address conflicts from differing subjects by introducing a scale-information remover. This ensures accurate cross-species animation. Moreover, we introduce two high-quality animal video datasets featuring a wide variety of species. Trained on these extensive datasets, our model is capable of generating videos characterized by accurate movements, consistent appearance, and high-fidelity frames, without the need for the pre-inference fine-tuning that prior arts required. Extensive experiments showcase the outstanding performance of our method in cross-species action following tasks, demonstrating exceptional shape adaptation capability. The project page is available at https://justinxu0.github.io/AnimateZoo/.

Related papers

• Zero-shot High-fidelity and Pose-controllable Character Animation [89.74818983864832]
  Image-to-video (I2V) generation aims to create a video sequence from a single image. Existing approaches suffer from inconsistency of character appearances and poor preservation of fine details. We propose PoseAnimate, a novel zero-shot I2V framework for character animation.
  arXiv  Detail & Related papers  (2024-04-21T14:43:31Z)
• AnimateZero: Video Diffusion Models are Zero-Shot Image Animators [63.938509879469024]
  We propose AnimateZero to unveil the pre-trained text-to-video diffusion model, i.e., AnimateDiff. For appearance control, we borrow intermediate latents and their features from the text-to-image (T2I) generation. For temporal control, we replace the global temporal attention of the original T2V model with our proposed positional-corrected window attention.
  arXiv  Detail & Related papers  (2023-12-06T13:39:35Z)
• Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation [27.700371215886683]
  diffusion models have become the mainstream in visual generation research, owing to their robust generative capabilities. In this paper, we propose a novel framework tailored for character animation. By expanding the training data, our approach can animate arbitrary characters, yielding superior results in character animation compared to other image-to-video methods.
  arXiv  Detail & Related papers  (2023-11-28T12:27:15Z)
• MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model [74.84435399451573]
  This paper studies the human image animation task, which aims to generate a video of a certain reference identity following a particular motion sequence. Existing animation works typically employ the frame-warping technique to animate the reference image towards the target motion. We introduce MagicAnimate, a diffusion-based framework that aims at enhancing temporal consistency, preserving reference image faithfully, and improving animation fidelity.
  arXiv  Detail & Related papers  (2023-11-27T18:32:31Z)
• SketchBetween: Video-to-Video Synthesis for Sprite Animation via Sketches [0.9645196221785693]
  2D animation is a common factor in game development, used for characters, effects and background art. Automated animation approaches exist, but are designed without animators in mind. We propose a problem formulation that adheres more closely to the standard workflow of animation.
  arXiv  Detail & Related papers  (2022-09-01T02:43:19Z)
• Deep Animation Video Interpolation in the Wild [115.24454577119432]
  In this work, we formally define and study the animation video code problem for the first time. We propose an effective framework, AnimeInterp, with two dedicated modules in a coarse-to-fine manner. Notably, AnimeInterp shows favorable perceptual quality and robustness for animation scenarios in the wild.
  arXiv  Detail & Related papers  (2021-04-06T13:26:49Z)
• 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)
• First Order Motion Model for Image Animation [90.712718329677]
  Image animation consists of generating a video sequence so that an object in a source image is animated according to the motion of a driving video. Our framework addresses this problem without using any annotation or prior information about the specific object to animate.
  arXiv  Detail & Related papers  (2020-02-29T07:08:56Z)

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.