Temporal interpolation often plays a crucial role to learn meaningful representations in dynamic scenes. In this paper, we propose a novel method to train spatiotemporal neural radiance fields of dynamic scenes based on temporal interpolation of feature vectors. Two feature interpolation methods are suggested depending on underlying representations, neural networks or grids. In the neural representation, we extract features from space-time inputs via multiple neural network modules and interpolate them based on time frames. The proposed multi-level feature interpolation network effectively captures features of both short-term and long-term time ranges. In the grid representation, space-time features are learned via four-dimensional hash grids, which remarkably reduces training time. The grid representation shows more than 100x faster training speed than the previous neural-net-based methods while maintaining the rendering quality. Concatenating static and dynamic features and adding a simple smoothness term further improve the performance of our proposed models. Despite the simplicity of the model architectures, our method achieved state-of-the-art performance both in rendering quality for the neural representation and in training speed for the grid representation.

For human pose estimation in videos, it is significant how to use temporal information between frames. In this paper, we propose temporal flow maps for limbs (TML) and a multi-stride method to estimate and track human poses. The proposed temporal flow maps are unit vectors describing the limbs' movements. We constructed a network to learn both spatial information and temporal information end-to-end. Spatial information such as joint heatmaps and part affinity fields is regressed in the spatial network part, and the TML is regressed in the temporal network part. We also propose a data augmentation method to learn various types of TML better. The proposed multi-stride method expands the data by randomly selecting two frames within a defined range. We demonstrate that the proposed method efficiently estimates and tracks human poses on the PoseTrack 2017 and 2018 datasets.

Non-rigid structure from motion (NRSfM) is a fundamental problem of computer vision. Recently, it has been shown that incorporating shape alignment in NRSfM can improve the performance significantly compared with the other algorithms, which do not consider shape alignment. However, realizing this idea was at a cost of a heavy, complicated process, which limits its usefulness and possible extensions. In this paper, we propose a novel regression framework for NRSfM, of which the variables (3D shapes) are regularized based on their aligned shapes. We show that this can be casted into an unconstrained problem or a problem with simple bound constraints, which can be efficiently solved by existing solvers. This framework can be easily integrated with numerous existing models and assumptions, such as orthographic or perspective camera models, occlusion, low-rank assumption, smooth deformations, and so on, which makes it more practical for various real situations. The experimental results show that the proposed method gives competitive result to the state-of-the-art methods for orthographic projection with much less time complexity and memory requirement, and outperforms the existing methods for perspective projection.

In this paper, a novel optical flow algorithm which is robust to illumination variation is proposed. HSL color space is adopted to decouple illumination and chromaticity information. The chromaticity component is normalized by chroma and transformed to the cartesian coordinate. Then, the decoupled distance is defined using both illumination and chromaticity. Cost function for the optical flow is formulated using l1norm of the decoupled distance with Huber norm regularization term. The cost function is efficiently minimized by utilizing Legendre-Fenchel transform. Optical flow field is further refined via weighted median filter whose weight is also based on the decoupled distance. Experimental results show that the proposed method works robustly even in the presence of severe illumination variation.