We introduce context-aware translation, a novel method that combines the benefits of inpainting and image-to-image translation, respecting simultaneously the original input and contextual relevance -- where existing methods fall short. By doing so, our method opens new avenues for the controllable use of AI within artistic creation, from animation to digital art. As an use case, we apply our method to redraw any hand-drawn animated character eyes based on any design specifications - eyes serve as a focal point that captures viewer attention and conveys a range of emotions, however, the labor-intensive nature of traditional animation often leads to compromises in the complexity and consistency of eye design. Furthermore, we remove the need for production data for training and introduce a new character recognition method that surpasses existing work by not requiring fine-tuning to specific productions. This proposed use case could help maintain consistency throughout production and unlock bolder and more detailed design choices without the production cost drawbacks. A user study shows context-aware translation is preferred over existing work 95.16% of the time.

翻译：我们提出上下文感知翻译方法，该方法融合了图像修复与图像到图像翻译的优势，同时兼顾原始输入内容与上下文相关性——这是现有方法的不足。通过这一方法，我们为人工智能在艺术创作中的可控应用开辟了新途径，涵盖动画制作到数字艺术领域。以应用案例为例，我们采用该方法根据任意设计规范重绘手绘动画角色的眼睛——眼睛作为捕捉观众注意力并传达丰富情感的核心焦点，然而传统动画的高劳动强度常导致眼睛设计的复杂度与一致性被迫妥协。此外，我们消除了训练所需的生产数据依赖，提出无需针对特定作品进行微调即可超越现有成果的新型角色识别方法。这一应用场景既能帮助维持作品制作的一致性，又能突破更大胆、更精细的设计选择，同时避免制作成本负担。用户研究表明，上下文感知翻译在95.16%的情况下比现有方法更受青睐。