让2D转3D更像电影，不只准还要有艺术感
Beyond Geometry: Artistic Disparity Synthesis for Immersive 2D-to-3D
- 分离全局深度与局部艺术效果，模仿导演创作意图
- 在专业3D电影数据上学习，实现画面弹出等关键效果
- 适合影视制作、艺术化3D转换场景
当前2D转3D方法虽几何准确，却缺乏艺术表现力，无法复现专业3D电影的沉浸感与情感冲击。这是因为几何重建范式将导演刻意设计的零平面偏移（用于突出效果）和局部深度塑造误判为噪声。本文提出新范式：艺术差异合成，目标从物理准确的视差估计转向艺术一致的视差生成。提出Art3D框架，采用双路径结构分离全局深度参数（宏观意图）与局部艺术效果（视觉笔触），通过间接监督学习专业3D电影数据。引入初步评估方法量化电影风格对齐度。实验表明，该方法能有效复现关键局部出屏效果，并匹配专业3D内容的全局深度风格，为艺术驱动型转换工具奠定基础。
原文摘要 · Abstract (English)
Current 2D-to-3D conversion methods achieve geometric accuracy but are artistically deficient, failing to replicate the immersive and emotionally resonant experience of professional 3D cinema. This is because geometric reconstruction paradigms mistake deliberate artistic intent, such as strategic zero-plane shifts for pop-out effects and local depth sculpting, for data noise or ambiguity. This paper argues for a new paradigm: Artistic Disparity Synthesis, shifting the goal from physically accurate disparity estimation to artistically coherent disparity synthesis. We propose Art3D, a preliminary framework exploring this paradigm. Art3D uses a dual-path architecture to decouple global depth parameters (macro-intent) from local artistic effects (visual brushstrokes) and learns from professional 3D film data via indirect supervision. We also introduce a preliminary evaluation method to quantify cinematic alignment. Experiments show our approach demonstrates potential in replicating key local out-of-screen effects and aligning with the global depth styles of cinematic 3D content, laying the groundwork for a new class of artistically-driven conversion tools.
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