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- Learning Continuous Image Representation with Local Implicit Image Function1,024
This paper proposes Local Implicit Image Function (LIIF), which takes an image coordinate and the 2D deep features around the coordinate as inputs, predicts the RGB value at a given coordinate as an output and builds a bridge between discrete and continuous representation in 2D.
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- GroupViT: Semantic Segmentation Emerges from Text Supervision783
A hierarchical Grouping Vision Transformer (GroupViT), which goes beyond the regular grid structure representation and learns to group image regions into progressively larger arbitrary-shaped segments and performs competitively to state-of-the-art transfer-learning methods requiring greater levels of supervision.
- Generative Face Completion645
This paper demonstrates qualitatively and quantitatively that the proposed effective face completion algorithm is able to deal with a large area of missing pixels in arbitrary shapes and generate realistic face completion results.
- Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models512
ODISE is presented, which unifies pre-trained text-image diffusion and discriminative models to perform open-vocabulary panoptic segmentation and outperforms the previous state of the art by significant margins on both open- VocabularyPanoptic and semantic segmentation tasks.
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- COLMAP-Free 3D Gaussian Splatting273
This paper leverages both the explicit geometric representation and the continuity of the input video stream to perform novel view synthesis without any SfM preprocessing and significantly improves over previous approaches in view synthesis and camera pose estimation under large motion changes.
- NVILA: Efficient Frontier Visual Language Models260
NVILA, a family of open VLMs designed to optimize both efficiency and accuracy, is introduced by improving its model architecture by first scaling up the spatial and temporal resolutions, and then compressing visual tokens, which enables NVILA to efficiently process high-resolution images and long videos.
- Deep Cascaded Bi-Network for Face Hallucination240
A novel framework for hallucinating faces of unconstrained poses and with very low resolution, which allows exceptional hallucination quality on in-the-wild low-res faces with significant pose and illumination variations is presented.
- Learning Dual Convolutional Neural Networks for Low-Level Vision221
The proposed DualCNN consists of two parallel branches, which respectively recovers the structures and details in an end-to-end manner, and can generate the target signals according to the formation model for each particular application.
- Semi-Supervised 3D Hand-Object Poses Estimation with Interactions in Time216
This work builds a joint learning framework where the spatial-temporal consistency in large-scale hand-object videos as a constraint for generating pseudo labels in semi-supervised learning and substantially improves hand pose estimation in challenging real-world dataset.
- Self-supervised Single-View 3D Reconstruction via Semantic Consistency194
This work is the first to try and solve the single-view reconstruction problem without a category-specific template mesh or semantic keypoints, and demonstrates that the unsupervised method performs comparably if not better than existing category-specific reconstruction methods learned with supervision.
- Synthesizing Long-Term 3D Human Motion and Interaction in 3D Scenes178
This paper presents a hierarchical generative framework to synthesize long-term 3D human motion conditioning on the 3D scene structure and further enforce multiple geometry constraints between the human mesh and scene point clouds via optimization to improve realistic synthesis.
- CamCo: Camera-Controllable 3D-Consistent Image-to-Video Generation175
This work introduces CamCo, which allows fine-grained Camera pose Control for image-to-video generation and significantly improves 3D consistency and camera control capabilities compared to previous models while effectively generating plausible object motion.
- Learning Recursive Filters for Low-Level Vision via a Hybrid Neural Network169
Experimental results show that many low-level vision tasks can be effectively learned and carried out in real-time by the proposed algorithm and is significantly smaller and faster in comparison with a deep CNN based image filter.
- SCOPS: Self-Supervised Co-Part Segmentation162
This work proposes a self-supervised deep learning approach for part segmentation, where several loss functions are devised that aids in predicting part segments that are geometrically concentrated, robust to object variations and are also semantically consistent across different object instances.
- Cosmos 3: Omnimodal World Models for Physical AI150
This report introduces Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences within a unified mixture-of-transformers architecture and demonstrates omnimodal world models as scalable, general-purpose backbones for embodied agents.
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- Affordance Diffusion: Synthesizing Hand-Object Interactions140
This work proposes a two-step generative approach: a LayoutNet that samples an articulation-agnostic hand-object-interaction layout, and a ContentNet that synthesizes images of a hand grasping the object given the predicted layout, built on top of a large-scale pretrained diffusion model to make use of its latent representation.
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- RGBD Objects in the Wild: Scaling Real-World 3D Object Learning from RGB-D Videos133
WildRGB-D comprises large-scale category-level RGB-D object videos, which are taken using an iPhone to go around the objects in 360 degrees and shows that the large-scale capture of RGB-D objects provides a large potential to advance 3D object learning.
- Putting Humans in a Scene: Learning Affordance in 3D Indoor Environments129
This work builds a fully automatic 3D pose synthesizer that fuses semantic knowledge from a large number of 2D poses extracted from TV shows as well as 3D geometric knowledge from voxel representations of indoor scenes to predict semantically plausible and physically feasible human poses within a given scene.
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- Describe Anything: Detailed Localized Image and Video Captioning107
The Describe Anything Model (DAM), a model designed for detailed localized captioning (DLC), is introduced and sets new state-of-the-art on 7 benchmarks spanning keywordlevel, phrase-level, and detailed multi-sentence localized image and video captioning.
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 2026-10-14. 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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