用锚帧保持角色视频长时间一致性，效果优于现有方法。
Gloria: Consistent Character Video Generation via Content Anchors
- 用少量锚帧捕捉角色视觉特征，稳定生成一致性外观。
- 支持超过10分钟长视频生成，多视角外观与身份高度一致。
- 适合影视动画、虚拟人开发等需要长期角色连贯性的场景。
数字角色在现代媒体中至关重要，但生成长时间、多视角一致且具表现力的角色视频仍具挑战。现有方法或缺乏足够上下文维持身份，或依赖非角色中心信息作为记忆，导致一致性不足。本文提出通过一组紧凑的锚帧表征角色视觉属性，提供稳定参考。针对基于参考的生成易出现复制粘贴和多锚冲突的问题，引入超集内容锚定机制，利用训练内外片段提示防止重复；并采用旋转位置编码作为弱条件，编码位置偏移以区分多个锚帧。此外，构建可扩展的流水线从海量视频中提取锚帧。实验表明，该方法可生成超过10分钟的高质量角色视频，在跨视角外观与身份表达上均优于现有方法。
原文摘要 · Abstract (English)
Digital characters are central to modern media, yet generating character videos with long-duration, consistent multi-view appearance and expressive identity remains challenging. Existing approaches either provide insufficient context to preserve identity or leverage non-character-centric information as the memory, leading to suboptimal consistency. Recognizing that character video generation inherently resembles an outside-looking-in scenario. In this work, we propose representing the character visual attributes through a compact set of anchor frames. This design provides stable references for consistency, while reference-based video generation inherently faces challenges of copy-pasting and multi-reference conflicts. To address these, we introduce two mechanisms: Superset Content Anchoring, providing intra- and extra-training clip cues to prevent duplication, and RoPE as Weak Condition, encoding positional offsets to distinguish multiple anchors. Furthermore, we construct a scalable pipeline to extract these anchors from massive videos. Experiments show our method generates high-quality character videos exceeding 10 minutes, and achieves expressive identity and appearance consistency across views, surpassing existing methods.
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