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MVAnimate: Enhancing Character Animation with Multi-View Optimization

Tianyu Sun, Zhoujie Fu, Bang Zhang, Guosheng Lin

TL;DR

MVAnimate presents a diffusion-based framework that cohesively integrates 2D/3D pose cues with multi-view priors to produce high-quality character animation. It introduces a Multi-View Pose Guidance Network with adaptive view weighting and attention alignment, plus a dedicated Multi-View Optimization module that enforces temporal, pose, and semantic coherence across views. The approach decouples appearance from motion during training to mitigate texture distortion and leverages data augmentation to address limited datasets. Across TikTok and TED-talks datasets, MVAnimate demonstrates superior 2D quality metrics and improved multi-view consistency, indicating strong practical potential for robust, view-aware character animation. The work also provides theoretical guarantees for the optimization process through inverse-variance weighting, convexity, and monotone descent analyses.

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.

MVAnimate: Enhancing Character Animation with Multi-View Optimization

TL;DR

MVAnimate presents a diffusion-based framework that cohesively integrates 2D/3D pose cues with multi-view priors to produce high-quality character animation. It introduces a Multi-View Pose Guidance Network with adaptive view weighting and attention alignment, plus a dedicated Multi-View Optimization module that enforces temporal, pose, and semantic coherence across views. The approach decouples appearance from motion during training to mitigate texture distortion and leverages data augmentation to address limited datasets. Across TikTok and TED-talks datasets, MVAnimate demonstrates superior 2D quality metrics and improved multi-view consistency, indicating strong practical potential for robust, view-aware character animation. The work also provides theoretical guarantees for the optimization process through inverse-variance weighting, convexity, and monotone descent analyses.

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.
Paper Structure (40 sections, 4 theorems, 22 equations, 10 figures, 6 tables)

This paper contains 40 sections, 4 theorems, 22 equations, 10 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Character Animation
  4. Multi-View Video Generation
  5. Preliminary
  6. Task Setting
  7. Diffusion Model Training
  8. Methodology
  9. Pipeline Overview
  10. Multi-View Pose Guidance Network
  11. Motivation for Adaptive View Weighting
  12. Network Details
  13. Theoretical Justification for Attention Alignment
  14. Multi-View Optimization Module
  15. Experimental Results
  16. ...and 25 more sections

Key Result

Proposition 3.1

Let $z_t=z_0+\sigma_t \epsilon$ with $\epsilon\sim\mathcal{N}(0,I)$ and condition $\mathcal{G}$ given by multi-view features. The minimizer $\epsilon_\theta^\star=\arg\min_\theta \mathbb{E}\|\epsilon - \epsilon_\theta(z_t,\mathcal{G},t)\|_2^2$ satisfies $\epsilon_\theta^\star(z_t,\mathcal{G},t)=\mat

Figures (10)

  • Figure 1: Some common problems in related research works. Here we show three examples of some of the SOTA animation algorithms. In the first row, when the reference video involves complex gestures, it is common for the performance to get relatively poor. In the second row, the texture of the reference video occasionally affects the output video and distorts the generated frame texture.
  • Figure 2: Overview of the inference pipeline of our MVAnimate. Our MVAnimate consists of two stages: 1) multi-view guided coarse video generation, which includes a ReferenceNet branch to extract appearance features from the reference image, and a denoising UNet branch to learn the multi-view pose features from the reference multi-view videos, and 2) multi-view optimization, which refines the coarse video output. We add the legends of the basic modules and data flow on the bottom right.
  • Figure 3: Attention Alignment in Denoising U-Net. The module aligns temporal attention and MV attention for the reference videos. (a) depicts the structure of the multi-view attention scheme in the Multi-View Pose Guidance Network, and (b) is an overview of the attention alignment in Denoising U-Net.
  • Figure 4: Qualitative results on TikTok dancing dataset. We compare our method with two other SOTA character animation algorithms on different reference images and video frames from the TikTok dataset.
  • Figure 5: Qualitative results on TED-talks dataset. We compare our method with two other SOTA character animation algorithms on different reference images and video frames from the TED-talks dataset.
  • ...and 5 more figures

Theorems & Definitions (7)

  • Proposition 3.1: Optimal denoiser as conditional score
  • Proposition 4.1: Inverse-variance optimality of view weighting
  • Proposition 4.2: Convex surrogate
  • Theorem 4.3: Monotone descent of MV-Opt (block coordinate minimization)
  • proof
  • proof
  • proof