mlsanityData to 7 Oct 2026

Beyond Geometry: Artistic Disparity Synthesis for Immersive 2D-to-3D

arXiv 2603.05906 ↗First posted 6 Mar 2026cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 25 of its 50 references that are in the map. First posted on arXiv 6 Mar 2026.

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25 of 50 references.

References · 25

  1. 2019Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfercited by 1,484
  2. 2024Depth Anything V2cited by 1,385
  3. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 819
  4. 2016Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescuecited by 368
  5. 2023Inpaint Anything: Segment Anything Meets Image Inpaintingcited by 233
  6. 2024SEA-RAFT: Simple, Efficient, Accurate RAFT for Optical Flowcited by 169
  7. 2016Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural Networkscited by 72
  8. 2025DiffuEraser: A Diffusion Model for Video Inpaintingcited by 69
  9. 2021I2UV-HandNet: Image-to-UV Prediction Network for Accurate and High-fidelity 3D Hand Mesh Modelingcited by 52
  10. 2024StereoCrafter: Diffusion-based Generation of Long and High-fidelity Stereoscopic 3D from Monocular Videoscited by 36
  11. 2023A Conditional Denoising Diffusion Probabilistic Model for Point Cloud Upsamplingcited by 28
  12. 2025SpaceDrive: Infusing Spatial Awareness into VLM-based Autonomous Drivingcited by 28
13 more
  1. 2024An End-to-End Robust Point Cloud Semantic Segmentation Network with Single-Step Conditional Diffusion Modelscited by 22
  2. 2019Small Obstacle Avoidance Based on RGB-D Semantic Segmentationcited by 13
  3. 2025Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusioncited by 11
  4. 2018Deep Global-Relative Networks for End-to-End 6-DoF Visual Localization and Odometrycited by 10
  5. 2025M2SVid: End-to-End Inpainting and Refinement for Monocular-to-Stereo Video Conversioncited by 10
  6. 2024SpatialMe: Stereo Video Conversion Using Depth-Warping and Blend-Inpaintingcited by 7
  7. 2025RemedyGS: Defend 3D Gaussian Splatting against Computation Cost Attackscited by 7
  8. 2025Optimizing for the Shortest Path in Denoising Diffusion Modelcited by 6
  9. 2025Eye2Eye: A Simple Approach for Monocular-to-Stereo Video Synthesiscited by 6
  10. 2018A Unified Framework for Mutual Improvement of SLAM and Semantic Segmentationcited by 5
  11. 2026Token-level Data Selection for Safe LLM Fine-tuningcited by 3
  12. 2021Visual Comfort Aware-Reinforcement Learning for Depth Adjustment of Stereoscopic 3D Imagescited by 2
  13. 2023Learning Triangular Distribution in Visual Worldcited by 1

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