Beyond Geometry: Artistic Disparity Synthesis for Immersive 2D-to-3D
Ping Chen, Zezhou Chen, Xingpeng Zhang, Yanlin Qian, Huan Hu, Xiang Liu, Zipeng Wang, Xin Wang, Zhaoxiang Liu, Kai Wang, Shiguo Lian

TL;DR
This paper introduces Artistic Disparity Synthesis and the Art3D framework to enhance 2D-to-3D conversion by capturing artistic intent and cinematic effects, moving beyond mere geometric accuracy.
Contribution
It proposes a new paradigm for disparity synthesis focused on artistic coherence and introduces a dual-path architecture trained on professional 3D film data.
Findings
Demonstrates ability to replicate local out-of-screen effects
Aligns global depth styles with cinematic 3D content
Lays groundwork for artistically-driven 2D-to-3D conversion tools
Abstract
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…
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Taxonomy
Topics3D Shape Modeling and Analysis · Computer Graphics and Visualization Techniques · Advanced Vision and Imaging
