Dynamic object modeling is a critical challenge in 3D scene reconstruction. Previous methods typically maintain a canonical space to represent the object model, and a deformation field to express the object motion. However, this approach fails when the object undergoes large motions. The position variation caused by significant motion not only complicates the establishment of a canonical space, but also misleads the interpretation of the deformation field. To overcome the above weaknesses, we propose Motion Decoupled Dynamic 3D Gaussian Splatting (M5D-GS), the first 3D-GS model that separates motion and deformation modeling for dynamic object representation with large motion from a monocular camera. M5D-GS increases the practicality of 3D-GS, as it is common for objects to move, rotate, and deform simultaneously. Current datasets only contain object deformations with slight motions. We introduce a pipeline to reuse current benchmarks by adding large motions into the scene. We also introduce a new benchmark featuring several new scenes with complex motions, scenes augmented from previous datasets, and some real world recorded testcases, to fully demonstrate our improvements. Our M5D-GS significantly increases the accuracy under large motion scenarios while maintaining high rendering speed, which makes it suitable for dynamic object representation tasks including 4D novel view synthesis and real-time rendering.

Recent methods have made significant progress in optical flow estimation. However, the evaluation of these methods focuses mainly on improved accuracy in benchmarks and often overlooks the analysis of network robustness, which may be important in safety-critical scenarios such as autonomous driving. In this paper, we propose a novel method for robustness evaluation by modifying data from original benchmarks. Unlike previous benchmarks that focus on complex scenes, we propose to modify shape and texture of objects from the original images in order to analyze the sensitivity to these changes observed in the output. Our aim is to identify common failure cases of state-of-the-art (SOTA) methods to evaluate their robustness and understand their behaviors. We show that: Optical flow methods are more sensitive to shape changes than to texture changes; and optical flow methods tend to "remember" objects seen during training and may "ignore" the motion of unseen objects. Our experimental results and findings provide a more in-depth understanding of the behavior of recent optical flow methods. (See https://github.com/llesky/Robustness-Analysis-Optical-Flow).

Referring video object segmentation (RVOS) extracts objects from videos based on provided text narrations. Previous approaches typically work on all video frames simultaneously through offline processing, but this is not always possible. Offline processing becomes also ineffective for long videos, as directly cutting the video into short clips to fit memory limits results in the loss of temporal consistency. To make RVOS more applicable to real-world video streams or long video scenarios, we introduce a Filtering Framework for RVOS (FF-RVOS), the first model capable of operating in online, semi-online, and offline modes with just one training session. We redesign RVOS as a stochastic optimization problem and leverage filtering to optimize object states across temporal sequences. Our method enhances temporal consistency for video streams and when a long video is processed in shorter clip sequences due to memory limitations. FF-RVOS demonstrates superior performance compared to previous state-of-the-art methods on public benchmarks with clear improvements, especially when the video is cut into short clips for processing. Our framework can also be embedded into different offline methods to boost temporal consistency. The project page is https://github.com/haliphinx/FF-RVOS.

Video frame interpolation based on optical flow has made great progress in recent years. Most of the previous studies have focused on improving the quality of clean videos. However, many real-world videos contain large obstructions making the video discontinuous. To address this challenge, we propose our Obstruction Robustness Framework (ORF) that enhances the robustness of existing VFI networks in the face of large obstructions. The ORF contains two components: (1) A feature repair module that first captures ambiguous pixels in the synthetic frame by a region similarity map, then repairs them with a cross-overlap attention module. (2) A data augmentation strategy that enables the network to handle dynamic obstructions without extra data. To the best of our knowledge, this is the first work that explicitly addresses the error caused by large obstructions in video frame interpolation. By using previous state-of-the-art methods as backbones, our method does not only improve the results in original benchmarks but also significantly enhances the interpolation quality for videos with obstructions.

The goal of Referring Video Object Segmentation is to extract an object from a video clip based on a given expression. While previous methods have utilized the transformer’s multi-modal learning capabilities to aggregate information from different modalities, they have mainly focused on spatial information and paid less attention to temporal information. To enhance the learning of temporal information, we propose TCE-RVOS with a novel frame token fusion (FTF) structure and a novel instance query transformer (IQT). Our technical innovations maximize the potential information gain of videos over single images. Our contributions also include a new classification of two widely used validation datasets for investigation of challenging cases. Our experimental results demonstrate that TCERVOS effectively captures temporal information and outperforms the previous state-of-the-art methods by increasing the J&F score by 4.0 and 1.9 points using ResNet-50 and VSwin-Tiny as the backbone on Ref-Youtube-VOS, respectively, and +2.0 mAP on A2D-Sentences dataset by using VSwin-Tiny backbone. The code is available at https://github.com/haliphinx/TCE-RVOS