NormalCrafter: Learning Temporally Consistent Normals from Video Diffusion Priors

Abstract. Surface normal estimation serves as a cornerstone for a spectrum of computer vision applications. While numerous efforts have been devoted to static image scenarios, ensuring temporal coherence in video-based normal estimation remains a formidable challenge. Instead of merely augmenting existing methods with temporal components, we present NormalCrafter to leverage the inherent temporal priors of video diffusion models. To secure high-fidelity normal estimation across sequences, we propose Semantic Feature Regularization (SFR), which aligns diffusion features with semantic cues, encouraging the model to concentrate on the intrinsic semantics of the scene. Moreover, we introduce a two-stage training protocol that leverages both latent and pixel space learning to preserve spatial accuracy while maintaining long temporal context. Extensive evaluations demonstrate the efficacy of our method, showcasing a superior performance in generating temporally consistent normal sequences with intricate details from diverse videos. Overview. We model the video normal estimation task with a video diffusion model conditioned on input RGB frames. We propose Semantic Feature Regularization (SFR) L_reg to align the diffusion features with robust semantic representations from DINO encoder, encouraging the model to concentrate on the intrinsic semantics for accurate and detailed normal estimation. Our training protocol consists of two stages: 1. training the entire U-Net in the latent space with diffusion score matching L_DSM and SFR L_reg; 2. fine-tuning only the spatial layers in pixel space with angular loss L_angular and SFR L_reg.