Blurry video frame interpolation (BVFI), which aims to generate high-frame-rate clear videos from low-frame-rate blurry inputs, is a challenging yet significant task in computer vision. Current state-of-the-art approaches typically rely on linear or quadratic models to estimate intermediate motion. However, these methods often overlook depth variations that occur during fast object motion, leading to changes in object size and hindering interpolation performance.This paper proposes the Differential Curves-guided Blurry Video Frame Interpolation (DC-BVFI) framework, which leverages the differential curves theory to analyze and mitigate the effects of depth variations caused by object motion. Specifically, DC-BVFI consists of UBNet and MPNet. Unlike prior approaches that rely on optical flow for frame interpolation, MPNet is designed to estimate the 3D scene flow, which facilitates a more precise awareness of depth and velocity variations. Since scene flow cannot be directly inferred in the 2D frame space, UBNet is introduced to transform them into 3D point maps. Extensive experiments demonstrate that the proposed DC-BVFI framework surpasses state-of-the-art performance in simulated and real-world datasets.

Model editing aims to efficiently revise incorrect or outdated knowledge within LLMs without incurring the high cost of full retraining and risking catastrophic forgetting. Currently, most LLM editing datasets are confined to narrow knowledge domains and cover a limited range of editing evaluation. They often overlook the broad scope of editing demands and the diversity of ripple effects resulting from edits. In this context, we introduce \uniedit, a unified benchmark for LLM editing grounded in open-domain knowledge. First, we construct editing samples by selecting entities from 25 common domains across five major categories, utilizing the extensive triple knowledge available in open-domain knowledge graphs to ensure comprehensive coverage of the knowledge domains. To address the issues of generality and locality in editing, we design an Neighborhood Multi-hop Chain Sampling (NMCS) algorithm to sample subgraphs based on a given knowledge piece to entail comprehensive ripple effects to evaluate. Finally, we employ proprietary LLMs to convert the sampled knowledge subgraphs into natural language text, guaranteeing grammatical accuracy and syntactical diversity. Extensive statistical analysis confirms the scale, comprehensiveness, and diversity of our \uniedit benchmark. We conduct comprehensive experiments across multiple LLMs and editors, analyzing their performance to highlight strengths and weaknesses in editing across open knowledge domains and various evaluation criteria, thereby offering valuable insights for future research endeavors.

Motion in the real world takes place in 3D space. Existing Frame Interpolation methods often estimate global receptive fields in 2D frame space. Due to the limitations of 2D space, these global receptive fields are limited, which makes it difficult to match object correspondences between frames, resulting in sub-optimal performance when handling large-motion scenarios. In this paper, we introduce a novel pipeline for exploring object correspondences based on differential surface theory. The differential surface coordinate system provides a better representation of the real world, enabling effective exploration of object correspondences. Specifically, the pipeline first transforms an input pair of video frames from the image coordinate system to the differential surface coordinate system. Subsequently, within this coordinate system, object correspondences are explored based on surface geometric properties and the surface uniqueness theorem. Experimental findings showcase that our method attains state-of-the-art performance across large motion benchmarks. Our method demonstrates the state-of-the-art performance on these VFI subsets with large motion.

Blurry video frame interpolation (BVFI) aims to generate high-frame-rate clear videos from low-frame-rate blurry videos, is a challenging but important topic in the computer vision community. Blurry videos not only provide spatial and temporal information like clear videos, but also contain additional motion information hidden in each blurry frame. However, existing BVFI methods usually fail to fully leverage all valuable information, which ultimately hinders their performance. In this paper, we propose a simple three-stage temporal deformable network to fully explore useful information from blurry videos. The frame interpolation stage designs a deformable network to directly sample useful information from blurry inputs and synthesize an intermediate frame at an arbitrary time interval. The temporal feature fusion stage explores the long-term temporal information for each target frame through a bi-directional recurrent deformable alignment network. And the deblurring stage applies a transformer-empowered Taylor approximation network to recursively recover the high-frequency details. Quantitative and qualitative results indicate that our model outperforms existing SOTA methods.