33 citations · 87 across the 16 of their papers we have counts for
16 papers
Synthetic-to-Real Translation for Class-Agnostic Motion Prediction
Yizheng Wu, Hongwei Fan, Kewei Wang +8
Motion understanding is critical for ensuring safety and robustness in autonomous driving systems, driving increasing interest in motion prediction. A key challenge in this domain…
Dynamic View Synthesis from Small Camera Motion Videos
Huiqiang Sun, Xingyi Li, Juewen Peng +4
Novel view synthesis for dynamic D scenes poses a significant challenge. Many notable efforts use NeRF-based approaches to address this task and yield impressive results. Howeve…
iControl3D: An Interactive System for Controllable 3D Scene Generation
Xingyi Li, Yizheng Wu, Jun Cen +6
3D content creation has long been a complex and time-consuming process, often requiring specialized skills and resources. While recent advancements have allowed for text-guided 3D…
Instance Consistency Regularization for Semi-Supervised 3D Instance Segmentation
Yizheng Wu, Zhiyu Pan, Kewei Wang +5
Large-scale datasets with point-wise semantic and instance labels are crucial to 3D instance segmentation but also expensive. To leverage unlabeled data, previous semi-supervised 3…
S-DyRF: Reference-Based Stylized Radiance Fields for Dynamic Scenes
Xingyi Li, Zhiguo Cao, Yizheng Wu +4
Current 3D stylization methods often assume static scenes, which violates the dynamic nature of our real world. To address this limitation, we present S-DyRF, a reference-based spa…
Self-Supervised Class-Agnostic Motion Prediction with Spatial and Temporal Consistency Regularizations
Kewei Wang, Yizheng Wu, Jun Cen +5
The perception of motion behavior in a dynamic environment holds significant importance for autonomous driving systems, wherein class-agnostic motion prediction methods directly pr…
DyBluRF: Dynamic Neural Radiance Fields from Blurry Monocular Video
Huiqiang Sun, Xingyi Li, Liao Shen +3
Recent advancements in dynamic neural radiance field methods have yielded remarkable outcomes. However, these approaches rely on the assumption of sharp input images. When faced wi…
Semi-Supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMix
Kewei Wang, Yizheng Wu, Zhiyu Pan +5
Class-agnostic motion prediction methods aim to comprehend motion within open-world scenarios, holding significance for autonomous driving systems. However, training a high-perform…
Make-It-4D: Synthesizing a Consistent Long-Term Dynamic Scene Video from a Single Image
Liao Shen, Xingyi Li, Huiqiang Sun +4
We study the problem of synthesizing a long-term dynamic video from only a single image. This is challenging since it requires consistent visual content movements given large camer…
SAD: Segment Any RGBD
Jun Cen, Yizheng Wu, Kewei Wang +6
The Segment Anything Model (SAM) has demonstrated its effectiveness in segmenting any part of 2D RGB images. However, SAM exhibits a stronger emphasis on texture information while…
3D Cinemagraphy from a Single Image
Xingyi Li, Zhiguo Cao, Huiqiang Sun +3
We present 3D Cinemagraphy, a new technique that marries 2D image animation with 3D photography. Given a single still image as input, our goal is to generate a video that contains…
SymmNeRF: Learning to Explore Symmetry Prior for Single-View View Synthesis
Xingyi Li, Chaoyi Hong, Yiran Wang +3
We study the problem of novel view synthesis of objects from a single image. Existing methods have demonstrated the potential in single-view view synthesis. However, they still fai…
Less is More: Consistent Video Depth Estimation with Masked Frames Modeling
Yiran Wang, Zhiyu Pan, Xingyi Li +3
Temporal consistency is the key challenge of video depth estimation. Previous works are based on additional optical flow or camera poses, which is time-consuming. By contrast, we d…
DoF-NeRF: Depth-of-Field Meets Neural Radiance Fields
Zijin Wu, Xingyi Li, Juewen Peng +3
Neural Radiance Field (NeRF) and its variants have exhibited great success on representing 3D scenes and synthesizing photo-realistic novel views. However, they are generally based…
Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report
Andrey Ignatov, Grigory Malivenko, David Plowman +35
Depth estimation is an important computer vision problem with many practical applications to mobile devices. While many solutions have been proposed for this task, they are usually…
The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern +1
Recently, there has been a significant interest in performing convolution over irregularly sampled point clouds. Since point clouds are very different from regular raster images, i…