Controllable Video Object Insertion via Multi-View Priors
Proposes multi-view priors for controllable video object insertion, enhancing identity consistency and scene integration.
Key Findings
Methodology
The approach lifts 2D reference images into multi-view 3D representations using Hunyuan3D 2.0, then employs view-consistent conditioning and a quality-aware weighting mechanism within a diffusion-based generative framework. A multi-view feature bank enables view-relevant texture and geometry retrieval, while semantic consistency checks via CLIP evaluate reconstruction reliability. The integration-aware module incorporates depth and contour heads for spatial grounding and a temporal loss for stability. These components collectively improve object identity preservation, spatial accuracy, and temporal coherence in dynamic scenes, without full scene reconstruction.
Key Results
- On DAVIS 2017, the method achieves PSNR 23.22 and SSIM 0.9026, outperforming VACE (PSNR 22.85, SSIM 0.9016). It also yields the lowest LPIPS and FVD scores, indicating superior perceptual quality and temporal realism.
- Spatially, Mask_IoU reaches 0.8138 and Box_IoU 0.9016, demonstrating precise localization. In text-driven scenarios, Box_IoU reaches 0.7405 with PSNR 21.27, showing effective cross-modal control.
- Ablation studies confirm that multi-view priors and quality-aware weighting significantly enhance identity consistency, boundary sharpness, and scene plausibility across diverse scenes.
Significance
This work advances video editing by integrating multi-view geometric and appearance priors, addressing longstanding issues like identity drift, boundary blurring, and scene inconsistency. Its ability to maintain object stability across viewpoints and over time opens new possibilities for high-fidelity scene synthesis, virtual content creation, and immersive media applications. The framework's modular design and reliance on geometric priors make it adaptable for real-world deployment, pushing the frontier of controllable, realistic video content generation.
Technical Contribution
Key innovations include: 1) a multi-view 3D object representation derived from reference images; 2) a view-consistent conditioning mechanism utilizing a feature bank for view-adaptive appearance retrieval; 3) an integration-aware module combining depth and contour supervision with temporal optimization. These enable high-quality, viewpoint-consistent object insertion without requiring full scene reconstruction, representing a significant step beyond existing single-view or weakly geometric methods.
Novelty
This is the first work to embed multi-view geometric priors directly into a diffusion-based video synthesis pipeline for object insertion. Unlike prior methods relying solely on 2D cues, it leverages multi-view features for spatial and temporal consistency, effectively handling viewpoint changes and occlusions. The combination of a view-adaptive retrieval system and spatial-temporal supervision offers a novel solution to longstanding challenges in scene realism and controllability.
Limitations
- Dependence on pre-trained 3D reconstruction models means quality drops with poor initial meshes, especially under severe occlusion or extreme viewpoints.
- Additional computational overhead from multi-view feature retrieval limits real-time application feasibility.
- Handling complex scene interactions, such as dynamic occlusions and non-rigid deformations, remains challenging, requiring further model enhancements.
Future Work
Future directions include integrating end-to-end multi-view reconstruction with generative models, optimizing retrieval efficiency for real-time applications, and extending the framework to handle more complex scene dynamics and multi-modal inputs like depth and motion cues. Exploring unsupervised or weakly supervised geometric priors could further improve robustness and scalability.
AI Executive Summary
Controllable video object insertion has long been a challenge due to the difficulty in maintaining object identity and scene realism across dynamic viewpoints. Existing methods often rely on single reference images or text prompts, which are insufficient to capture the full appearance and geometric variations of objects as the camera moves. This leads to issues such as identity drift, boundary artifacts, and temporal flickering, severely limiting practical applications in film editing, AR/VR, and autonomous driving.
To address these limitations, this work introduces a novel framework that leverages multi-view geometric priors. By lifting a 2D reference image into a multi-view 3D representation using Hunyuan3D 2.0, the system generates a set of view-specific renderings that provide richer appearance and geometric cues. These multi-view features are stored in a searchable feature bank, enabling view-adaptive retrieval of textures and shapes during generation. The core of the approach is a view-consistent conditioning module, which combines a shared latent encoder with cross-attention mechanisms to fuse multi-view information, ensuring the inserted object maintains consistent identity and appearance from different angles.
Further, the framework incorporates an integration-aware consistency module that supervises depth and contour predictions, improving spatial grounding and boundary sharpness. A temporal loss based on optical flow reduces flickering, ensuring smooth motion continuity. The entire system is trained with a combination of diffusion denoising loss, depth and boundary supervision, and semantic consistency checks via CLIP, resulting in a highly robust and controllable video editing pipeline.
Extensive experiments on datasets like DAVIS 2017, VIPSeg, and MagicBench demonstrate that the proposed method outperforms state-of-the-art baselines in visual quality, spatial accuracy, and temporal stability. Quantitative metrics such as PSNR, SSIM, and Mask_IoU show clear improvements, with PSNR reaching 23.22 and Mask_IoU exceeding 0.81. In text-driven scenarios, the system achieves a Box_IoU of 0.7405, validating its cross-modal control capabilities.
This research significantly advances the field by enabling high-fidelity, viewpoint-consistent object insertion without requiring full scene reconstruction. Its modular design and reliance on geometric priors open new avenues for realistic scene editing, virtual content creation, and immersive media applications. Future work will focus on optimizing computational efficiency, extending scene complexity handling, and integrating multi-modal cues for even more natural and interactive content generation.
Deep Analysis
Background
视频内容生成与编辑技术近年来快速发展，尤其在虚拟现实、影视后期和自动驾驶中，内容的真实性和控制性成为核心需求。早期方法如GAN和扩散模型能合成高质量视频，但在对象插入方面存在空间定位不精确、时间不连续等问题。空间条件引导（如点轨迹、边界框、掩码）提升了控制能力，但多依赖单视角信息，难以应对复杂视角变化带来的外观漂移。近年来，结合多视角几何信息的研究逐渐兴起，代表性工作如MVHOI尝试融合多视角几何与外观信息支持复杂场景中的人-物交互，但未能充分解决深度关系和边界融合问题。整体来看，视频插入的空间一致性和时间稳定性仍是技术难点。
Core Problem
核心问题在于如何在动态场景中实现对象的空间一致性和外观稳定性，尤其在视角变化、遮挡和复杂背景中。传统单视角方法难以保证对象在不同角度下的外观一致，容易出现身份漂移和边界模糊。此外，缺乏对深度关系和遮挡关系的建模，导致前景与背景融合不自然，场景空间合理性不足。解决该问题需要引入多视角几何信息，结合空间-时间一致性机制，确保对象在多视角、多时间点的连续性和真实感。
Innovation
本研究的创新点包括：1）引入多视角几何先验，将二维参考图升维为多视角3D模型，丰富对象空间信息；2）设计视角一致性条件化机制，通过多视角特征库实现视角相关的外观检索，增强空间一致性；3）融合感知一致性模块，结合深度和轮廓引导，优化前景-背景融合的空间连续性和边界清晰度。这些创新突破了传统单视角限制，显著提升了动态场景中对象插入的真实性和控制性。
Methodology
- �� 输入：二维参考图像、视频序列。• 3D重建：利用Hunyuan3D 2.0将参考图升维为 textured mesh，生成多视角渲染图。• 多视角特征库：编码多视角渲染图，构建可检索的知识库。• 条件化机制：引入视角一致性条件化模块，包括• 双路径参考注入：一是共享编码器提取全局外观特征，二是通过特征库实现视角相关的纹理检索。• 质量感知加权：利用CLIP语义一致性评估视角重建质量，动态调节特征库贡献。• 融合一致性：引入深度和轮廓引导模块，优化空间布局。• 时序优化：利用光流信息，减少帧间抖动，确保时间连续性。• 训练目标：结合扩散模型的去噪损失、深度与边界监督、时序一致性损失，整体优化模型性能。
Experiments
采用DAVIS 2017、VIPSeg和MagicBench等公开数据集，评估指标包括PSNR、SSIM、LPIPS、FVD、Mask_IoU和Box_IoU。对比多项基线方法，验证模型在空间定位、时间连续性和身份保持方面的优越性。通过消融实验，分析多视角特征库、质量感知机制和空间-时间优化的贡献。参数设置包括多视角渲染视图数、CLIP语义阈值和训练轮次，确保模型在不同场景下的鲁棒性。
Results
实验显示，提出方法在PSNR达23.22、SSIM达0.9026，明显优于VACE的22.85和0.9016。Mask_IoU提升至0.8138，Box_IoU 0.9016，验证空间定位精度。文本引导场景中，Box_IoU达0.7405，PSNR为21.27，显示良好的跨模态控制能力。消融分析表明，多视角特征库和质量感知机制对提升身份一致性和边界清晰度起到关键作用。整体结果证明该框架在视觉质量、空间合理性和时间稳定性方面优于现有技术。
Applications
该技术适用于影视后期、虚拟现实、增强现实和自动驾驶模拟等场景，可实现高质量、可控的场景内容编辑。只需提供参考图像和控制信号，即可在复杂动态背景中插入目标对象，提升内容的真实感和交互性。未来还可结合实时多视角重建，支持实时场景编辑和多模态交互，推动虚拟内容生成的智能化发展。
Limitations & Outlook
当前模型依赖预训练的3D重建质量，极端视角或遮挡严重时效果下降。多视角特征检索计算成本较高，实时应用尚需优化。对复杂场景中的遮挡关系和动态交互建模仍有提升空间，未来需结合更强的几何推理机制。
Plain Language Accessible to non-experts
想象你在厨房做饭，要加入不同的调料和食材。每次加入的调料都要看它在不同锅里的样子，不能只看一角。这个方法就像用多个视角观察食材，确保它在每个角度都看起来新鲜、好吃。传统方法只用一张图片，容易看走眼，做出来的菜可能味道不一致或不自然。而这个新方法用多角度信息，就像用多个摄像头同时观察食材，确保每个角度都完美无瑕。这样做出来的菜，不仅看起来更真实，还能在不同角度都保持一致，就像电影里的特效一样自然。它还会用深度和轮廓线帮忙，把菜的边界画得更清楚，避免模糊。最终，这个技术让虚拟场景中的物体插入变得像真实一样自然，既可以用文字描述，也可以用图片指导，未来还能实现实时编辑，就像在游戏里随意添加新角色一样方便。
Abstract
Video object insertion places a user-specified object in an existing dynamic scene. Existing methods typically condition generation on text or a single reference image. Consequently, object appearance is underconstrained under viewpoint changes, often leading to identity drift, incorrect foreground-background layering, boundary artifacts, and temporal flickering. In this paper, we propose a video object insertion framework that incorporates multi-view object priors to address these limitations. The framework lifts a 2D reference image into a multi-view representation and uses view-consistent conditioning to provide stable identity guidance and view-adaptive appearance cues. A quality-aware weighting mechanism reduces the influence of noisy or imperfect reconstructed views. We further introduce an Integration-Aware Consistency Module that promotes plausible occlusion, clean boundaries, and temporal continuity. Experiments demonstrate that the proposed framework improves visual quality, controllability, identity consistency, and foreground-background integration for video object insertion compared to the baseline methods. Project page: https://polarisxq.github.io/MOVI/.