用学习方法快速生成3D人像模板并减少动画变形失真。
FastAnimate: Towards Learnable Template Construction and Pose Deformation for Fast 3D Human Avatar Animation
- 用U-Net分离姿态与纹理信息，快速生成人像模板。
- 通过数据驱动优化提升姿态变形的结构完整性。
- 在多种姿势下表现稳定，速度与质量优于现有方法。
3D人像动画旨在通过形变算法将人像从任意初始姿态转换到目标姿态。现有方法通常分为两个阶段：标准模板构建与目标姿态形变。然而，当前模板构建方法依赖大量骨骼绑定，常在特定姿态下产生伪影；而目标姿态形变受线性混合蒙皮（LBS）影响，导致结构失真，严重影响动画真实感。为此，我们提出一个统一的学习框架，分两阶段解决上述问题。第一阶段，采用U-Net架构，在前向传播中解耦纹理与姿态信息，实现快速生成人像模板。第二阶段，提出一种数据驱动的精修技术，增强形变过程中的结构完整性。大量实验表明，该模型在多种姿态下均保持一致性能，效率与质量达到最优平衡，超越现有最先进方法。
原文摘要 · Abstract (English)
3D human avatar animation aims at transforming a human avatar from an arbitrary initial pose to a specified target pose using deformation algorithms. Existing approaches typically divide this task into two stages: canonical template construction and target pose deformation. However, current template construction methods demand extensive skeletal rigging and often produce artifacts for specific poses. Moreover, target pose deformation suffers from structural distortions caused by Linear Blend Skinning (LBS), which significantly undermines animation realism. To address these problems, we propose a unified learning-based framework to address both challenges in two phases. For the former phase, to overcome the inefficiencies and artifacts during template construction, we leverage a U-Net architecture that decouples texture and pose information in a feed-forward process, enabling fast generation of a human template. For the latter phase, we propose a data-driven refinement technique that enhances structural integrity. Extensive experiments show that our model delivers consistent performance across diverse poses with an optimal balance between efficiency and quality,surpassing state-of-the-art (SOTA) methods.
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