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Claim this profileWorks185 from public data
- 2D Gaussian Splatting for Geometrically Accurate Radiance Fields1,708
2D Gaussian Splatting (2DGS), a novel approach to model and reconstruct geometrically accurate radiance fields from multi-view images from multi-view images by introducing a perspective-accurate 2D splatting process utilizing ray-splat intersection and rasterization.
- Future Frame Prediction for Anomaly Detection - A New Baseline1,542
This paper proposes to tackle the anomaly detection problem within a video prediction framework by introducing a motion (temporal) constraint in video prediction by enforcing the optical flow between predicted frames and ground truth frames to be consistent, and is the first work that introduces a temporal constraint into the video prediction task.
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- InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models569
InstantMesh is presented, a feed-forward framework for instant 3D mesh generation from a single image, featuring state-of-the-art generation quality and significant training scalability, and is able to create diverse 3D assets within 10 seconds.
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- Structured3D: A Large Photo-Realistic Dataset for Structured 3D Modeling462
This paper presents a new synthetic dataset, Structured3D, with the aim of providing large-scale photo-realistic images with rich 3D structure annotations for a wide spectrum of structured 3D modeling tasks, and takes advantage of the availability of professional interior designs to automatically extract 3D structures from them.
- Kernel Sparse Representation for Image Classification and Face Recognition386
KSR is essentially the sparse coding technique in a high dimensional feature space mapped by implicit mapping function that outperforms sparse coding and EMK, and achieves state-of-the-art performance for image classification and face recognition on publicly available datasets.
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- Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesis292
A 3D body mesh recovery module is proposed to disentangle the pose and shape, which can not only model the joint location and rotation but also characterize the personalized body shape and is able to support a more flexible warping from multiple sources.
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- Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton Refinement289
This paper presents a Fast-MVSNet, a novel sparse- to-dense coarse-to-fine framework, for fast and accurate depth estimation in MVS, while achieving comparable or even better results on the challenging Tanks and Temples dataset as well as the DTU dataset.
- Michelangelo: Conditional 3D Shape Generation based on Shape-Image-Text Aligned Latent Representation285
The proposed approach can generate higher-quality and more diverse 3D shapes that better semantically conform to the visual or textural conditional inputs, validating the effectiveness of the shape-image-text-aligned space for cross-modality 3D shape generation.
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- Appearance-Motion Memory Consistency Network for Video Anomaly Detection240
This work proposes an Appearance-Motion Memory Consistency Network (AMMC-Net), which first makes full use of the prior knowledge of appearance and motion signals to explicitly capture the correspondence between them in the high-level feature space, and combines the multi-view features to obtain a more essential and robust feature representation of regular events.
- Learning to Parse Wireframes in Images of Man-Made Environments240
A learning-based approach to the task of automatically extracting a "wireframe" representation for images of cluttered man-made environments and two convolutional neural networks that are suitable for extracting junctions and lines with large spatial support are proposed.
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- Dream3D: Zero-Shot Text-to-3D Synthesis Using 3D Shape Prior and Text-to-Image Diffusion Models209
This paper makes the first attempt to introduce explicit 3D shape priors into the CLIP-guided 3D optimization process, and proposes to jointly optimize a learnable text prompt and fine-tune the text-to-image diffusion model for rendering-style image generation.
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- DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs154
A deep neural network model - DeepPROTACs is introduced to help design potent PROTACs molecules and can predict the degradation capacity of a proposed PROTAC molecule based on structures of given target protein and E3 ligase.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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