# DissolveStereo: Coarse Depth Injection for Zero-Shot Stereo Video Generation arXiv: https://arxiv.org/abs/2411.14295 ยท HTML: https://mlsanity.com/paper/2411.14295/ ## Key facts - Cited by 6 papers in the map (data through 14 Oct 2026). - Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models. - Builds on 31 of its 51 references that are in the map. - First posted on arXiv 21 Nov 2024. ## Placed in - Diffusion models / [3D, point cloud and scene generation](https://mlsanity.com/diffusion/3d-point-cloud-and-scene-generation/) ## Builds on (most cited first, 30 of 31) - [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020) - [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022) - [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019) - [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024) - [MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing](https://mlsanity.com/paper/2304.08465/) (2023) - [AnyDoor: Zero-shot Object-level Image Customization](https://mlsanity.com/paper/2307.09481/) (2023) - [ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis](https://mlsanity.com/paper/2409.02048/) (2024) - [Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Prior](https://mlsanity.com/paper/2303.14184/) (2023) - [LucidDreamer: Domain-free Generation of 3D Gaussian Splatting Scenes](https://mlsanity.com/paper/2311.13384/) (2023) - [Consistent Video Depth Estimation](https://mlsanity.com/paper/2004.15021/) (2020) - [3D Photography using Context-aware Layered Depth Inpainting](https://mlsanity.com/paper/2004.04727/) (2020) - [DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos](https://mlsanity.com/paper/2409.02095/) (2024) - [Robust Consistent Video Depth Estimation](https://mlsanity.com/paper/2012.05901/) (2020) - [4D-fy: Text-to-4D Generation Using Hybrid Score Distillation Sampling](https://mlsanity.com/paper/2311.17984/) (2023) - [DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior](https://mlsanity.com/paper/2310.16818/) (2023) - [Text2NeRF: Text-Driven 3D Scene Generation with Neural Radiance Fields](https://mlsanity.com/paper/2305.11588/) (2023) - [Collaborative Video Diffusion: Consistent Multi-video Generation with Camera Control](https://mlsanity.com/paper/2405.17414/) (2024) - [RealmDreamer: Text-Driven 3D Scene Generation with Inpainting and Depth Diffusion](https://mlsanity.com/paper/2404.07199/) (2024) - [4Diffusion: Multi-view Video Diffusion Model for 4D Generation](https://mlsanity.com/paper/2405.20674/) (2024) - [Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural Networks](https://mlsanity.com/paper/1604.03650/) (2016) - [Web Stereo Video Supervision for Depth Prediction from Dynamic Scenes](https://mlsanity.com/paper/1904.11112/) (2019) - [4Real: Towards Photorealistic 4D Scene Generation via Video Diffusion Models](https://mlsanity.com/paper/2406.07472/) (2024) - [DreamMatcher: Appearance Matching Self-Attention for Semantically-Consistent Text-to-Image Personalization](https://mlsanity.com/paper/2402.09812/) (2024) - [Learning Stereo from Single Images](https://mlsanity.com/paper/2008.01484/) (2020) - [StereoCrafter: Diffusion-based Generation of Long and High-fidelity Stereoscopic 3D from Monocular Videos](https://mlsanity.com/paper/2409.07447/) (2024) - [HiFi-123: Towards High-fidelity One Image to 3D Content Generation](https://mlsanity.com/paper/2310.06744/) (2023) - [Generative Rendering: Controllable 4D-Guided Video Generation with 2D Diffusion Models](https://mlsanity.com/paper/2312.01409/) (2023) - [KV Inversion: KV Embeddings Learning for Text-Conditioned Real Image Action Editing](https://mlsanity.com/paper/2309.16608/) (2023) - [InFusion: Inject and Attention Fusion for Multi Concept Zero-Shot Text-based Video Editing](https://mlsanity.com/paper/2308.00135/) (2023) - [FEC: Three Finetuning-free Methods to Enhance Consistency for Real Image Editing](https://mlsanity.com/paper/2309.14934/) (2023) ## Built on by (most cited first, 6 of 6) - [SpatialMe: Stereo Video Conversion Using Depth-Warping and Blend-Inpainting](https://mlsanity.com/paper/2412.11512/) (2024) - [Restereo: Diffusion stereo video generation and restoration](https://mlsanity.com/paper/2506.06023/) (2025) - [StereoWorld: Geometry-Aware Monocular-to-Stereo Video Generation](https://mlsanity.com/paper/2512.09363/) (2025) - [StereoSpace: Depth-Free Synthesis of Stereo Geometry via End-to-End Diffusion in a Canonical Space](https://mlsanity.com/paper/2512.10959/) (2025) - [EPIC: Epipolar-Consistent 360deg Immersive Stereo Video Generation](https://mlsanity.com/paper/2609.38689/) (2026) - [StereoEngine: Scaling Stereo Bimanual Robot Data without Stereo Cameras](https://mlsanity.com/paper/2610.12734/) (2026) ## Abstract (arXiv) Generating high-quality stereo videos requires consistent depth perception and temporal coherence across frames. Despite advances in image and video synthesis using diffusion models, producing high-quality stereo videos remains a challenging task due to the difficulty of maintaining consistent temporal and spatial coherence between left and right views. We introduce DissolveStereo, a novel framework for zero-shot stereo video generation that leverages video diffusion priors without requiring paired training data. Our key innovations include a noisy restart strategy to initialize stereo-aware latent representations and an iterative refinement process that progressively harmonizes the latent space, addressing issues like temporal flickering and view inconsistencies. Importantly, we propose the use of dissolved depth maps to streamline latent space operations by reducing high-frequency depth information. Our comprehensive evaluations, including quantitative metrics and user studies, demonstrate that DissolveStereo produces high-quality stereo videos with enhanced depth consistency and temporal smoothness. In terms of epipolar consistency, our method achieves an 11.7% improvement in MEt3R score over the current state-of-the-art. Furthermore, user studies indicate strong perceptual gains over the previous arts, with an 8.0% higher perceived frame quality and 10.9% higher perceived temporal coherence. Our code is in https://github.com/shijianjian/DissolveStereo.