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.

Unsupervised learning of 3D-aware generative adversarial networks has lately made much progress. Some recent work demonstrates promising results of learning human generative models using neural articulated radiance fields, yet their generalization ability and controllability lag behind parametric human models, i.e., they do not perform well when generalizing to novel pose/shape and are not part controllable. To solve these problems, we propose VeRi3D, a generative human vertex-based radiance field parameterized by vertices of the parametric human template, SMPL. We map each 3D point to the local coordinate system defined on its neighboring vertices, and use the corresponding vertex feature and local coordinates for mapping it to color and density values. We demonstrate that our simple approach allows for generating photorealistic human images with free control over camera pose, human pose, shape, as well as enabling part-level editing.

Crowd counting is a concerned yet challenging task in computer vision. The difficulty is particularly pronounced by scale variations in crowd images. Most state-of-art approaches tackle the multi-scale problem by adopting multi-column CNN architectures where different columns are designed with different filter sizes to adapt to variable pedestrian/object sizes. However, the structure is bloated and inefficient, and it is infeasible to adopt multiple deep columns due to the huge resource cost. We instead propose a Scale Pyramid Network (SPN) which adopts a shared single deep column structure and extracts multi-scale information in high layers by Scale Pyramid Module. In Scale Pyramid Module, we specifically employ different rates of dilated convolutions in parallel instead of traditional convolutions with different sizes. Compared to other methods of coping with scale issues, our single column structure with Scale Pyramid Module can get more accurate estimation with simpler structure and less complexity of training. And our Scale Pyramid Module can be easily applied to a deep network. Experimental results on four datasets show that our method achieves state-of-the-art performance. On ShanghaiTech Part_A dataset which is challenging for its highly congested scenes and scale variation, we achieve 9.5% lower MAE and 13.5% lower MSE than the previous state-of-the-art method. We also extend our model on TRANCOS vehicle counting dataset and significantly achieve 5.9% lower GAME(0), 10% lower GAME(1), 24.5% lower GAME(2), 38.7% lower GAME(3) than the previous state-of-the-art method. The experimental results prove the robustness of our model for crowd counting, especially with scale variations.