利用多视角信息提升角色动画的时空一致性与画质。
MVAnimate: Enhancing Character Animation with Multi-View Optimization
- 融合多视角先验，同步生成2D与3D动态信息
- 在多个数据集上实现更连贯、更高质量的动画输出
- 适合需要高保真角色动画的影视与游戏开发者
角色动画的现实感与多样性需求日益增长，广泛应用于多个领域。然而，现有基于2D或3D结构建模人体姿态的动画生成算法普遍存在输出质量低、训练数据不足等问题，难以生成高质量动画视频。为此，我们提出MVAnimate，一种新颖框架，通过多视角先验信息融合2D与3D动态信息，提升生成视频质量。该方法利用多视角先验生成时间连续、空间一致的动画输出，在多个数据集上的实验结果表明其对多种运动模式与外观具有鲁棒性。此外，MVAnimate还优化目标角色的多视角视频，从不同视角显著提升画质。
原文摘要 · Abstract (English)
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.
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