MVAnimate: Enhancing Character Animation with Multi-View Optimization

• URL: http://arxiv.org/abs/2602.08753v1
• Date: Mon, 09 Feb 2026 14:55:21 GMT
• Title: MVAnimate: Enhancing Character Animation with Multi-View Optimization
• Authors: Tianyu Sun, Zhoujie Fu, Bang Zhang, Guosheng Lin,
• Abstract summary: We introduce MVAnimate, a novel framework that synthesizes both 2D and 3D information of dynamic figures based on multi-view prior information.<n>Our approach leverages multi-view prior information to produce temporally consistent and spatially coherent animation outputs.
• Score: 55.4217617472079
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The demand for realistic and versatile character animation has surged, driven by its wide-ranging applications in various domains. However, the animation generation algorithms modeling human pose with 2D or 3D structures all face various problems, including low-quality output content and training data deficiency, preventing the related algorithms from generating high-quality animation videos. Therefore, we introduce MVAnimate, a novel framework that synthesizes both 2D and 3D information of dynamic figures based on multi-view prior information, to enhance the generated video quality. Our approach leverages multi-view prior information to produce temporally consistent and spatially coherent animation outputs, demonstrating improvements over existing animation methods. Our MVAnimate also optimizes the multi-view videos of the target character, enhancing the video quality from different views. Experimental results on diverse datasets highlight the robustness of our method in handling various motion patterns and appearances.

Related papers

• UniMo: Unifying 2D Video and 3D Human Motion with an Autoregressive Framework [54.337290937468175]
  We propose UniMo, an autoregressive model for joint modeling of 2D human videos and 3D human motions within a unified framework.<n>We show that our method simultaneously generates corresponding videos and motions while performing accurate motion capture.
  arXiv  Detail & Related papers  (2025-12-03T16:03:18Z)
• Animating the Uncaptured: Humanoid Mesh Animation with Video Diffusion Models [71.78723353724493]
  Animation of humanoid characters is essential in various graphics applications.<n>We propose an approach to synthesize 4D animated sequences of input static 3D humanoid meshes.
  arXiv  Detail & Related papers  (2025-03-20T10:00:22Z)
• Alignment is All You Need: A Training-free Augmentation Strategy for Pose-guided Video Generation [19.408715783816167]
  We introduce a training-free framework that ensures the generated video sequence preserves the reference image's subtleties.<n>We decouple skeletal and motion priors from pose information, enabling precise control over animation generation.<n>Our method significantly enhances the quality of video generation without the need for large datasets or expensive computational resources.
  arXiv  Detail & Related papers  (2024-08-29T13:08:12Z)
• Towards Multiple Character Image Animation Through Enhancing Implicit Decoupling [77.08568533331206]
  We propose a novel multi-condition guided framework for character image animation.<n>We employ several well-designed input modules to enhance the implicit decoupling capability of the model.<n>Our method excels in generating high-quality character animations, especially in scenarios of complex backgrounds and multiple characters.
  arXiv  Detail & Related papers  (2024-06-05T08:03:18Z)
• 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)
• Video-Driven Animation of Neural Head Avatars [3.5229503563299915]
  We present a new approach for video-driven animation of high-quality neural 3D head models. We introduce an LSTM-based animation network capable of translating person-independent expression features into personalized animation parameters.
  arXiv  Detail & Related papers  (2024-03-07T10:13:48Z)
• 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)
• 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.