Sharper Contours: NormalCrafter Uses Video Diffusion Models for Temporally Consistent Normal Estimation

The estimation of surface normals is a fundamental process in computer vision and is used in areas such as 3D reconstruction, object tracking, and augmented reality. While normal estimation in single images is already quite advanced, temporally consistent calculation in videos presents a significant challenge. A new approach called NormalCrafter promises a remedy by utilizing the temporal information of video diffusion models.

Challenges of Temporal Consistency

Previous methods for normal estimation in videos often resort to post-processing temporal smoothing. However, this can lead to inaccuracies, especially with fast movements or complex scenes. NormalCrafter takes a different approach by directly integrating the inherent temporal information of video diffusion models into the calculation process. This ensures the temporal consistency of the calculated normals from the ground up.

Semantic Feature Regularization (SFR)

To further improve the quality of normal estimation, NormalCrafter uses what is called Semantic Feature Regularization (SFR). This technique balances the features of the diffusion model with semantic information. This encourages the model to focus on the essential semantic aspects of the scene and ignore irrelevant details. This leads to a more precise and robust normal estimation.

Two-Phase Training for Optimal Results

The training of NormalCrafter takes place in two phases. In the first phase, the model learns in latent space to capture the temporal context over longer sequences. In the second phase, training continues in pixel space to optimize the spatial accuracy of the normal estimation. This combination of latent and pixel-based learning allows NormalCrafter to ensure both temporal consistency and spatial precision.

Promising Results and Future Applications

Initial tests show that NormalCrafter delivers significantly better results in temporally consistent normal estimation compared to existing methods. The method generates detailed and stable normal sequences even in complex video scenes. These advances open up new possibilities for various applications in computer vision, including the more realistic representation of 3D objects in virtual environments and improved human-computer interaction in augmented reality applications. The integration of semantic information and the use of video diffusion models represent a promising step towards more robust and precise normal estimation in videos.

The Importance for AI-Powered Content Creation

The development of NormalCrafter highlights the potential of AI-based tools for content creation. By automating complex tasks such as normal estimation, creatives can save time and resources and focus on designing engaging content. Companies like Mindverse, which offer all-in-one solutions for AI-powered content creation, can benefit from such innovations and provide their customers with powerful tools.

Bibliography: - Bin, Y., Hu, W., Wang, H., Chen, X., & Wang, B. (2025). NormalCrafter: Learning Temporally Consistent Normals from Video Diffusion Priors. arXiv preprint arXiv:2504.11427. - Zürn, M., Brock, A., Hambach, L., & Learned-Miller, E. (2018). Depth from video, revisited. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 512-528). - Jha, S., Hu, W., Wang, H., Chen, X., & Wang, B. ChronoDepth. https://jhaoshao.github.io/ChronoDepth/