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Works69 from public data
- 3,320
- Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks1,364
This work introduces Grounded SAM, which uses Grounding DINO as an open-set object detector to combine with the segment anything model (SAM), which enables the detection and segmentation of any regions based on arbitrary text inputs and opens a door to connecting various vision models.
- Effective Whole-body Pose Estimation with Two-stages Distillation446
This work presents a two-stage pose Distillation for Whole-body Pose estimators, named DWPose, to improve their effectiveness and efficiency and releases a series of models with different sizes, from tiny to large, for satisfying various downstream tasks.
- FITS: Modeling Time Series with $10k$ Parameters376
FITS achieves performance comparable to state-of-the-art models for time series forecasting and anomaly detection tasks, while having a remarkably compact size of only approximately $10k$ parameters.
- Human Pose Regression with Residual Log-likelihood Estimation315
This work proposes a novel regression paradigm with Residual Log-likelihood Estimation (RLE) to capture the underlying output distribution and finds that for the first time, the proposed regression method is superior to the heatmap-based methods.
- SRNet: Improving Generalization in 3D Human Pose Estimation with a Split-and-Recombine Approach229
The proposed split-and-recombine approach, called SRNet, can be easily adapted to both single-image and temporal models, and it leads to appreciable improvements in the prediction of rare and unseen poses.
- Follow-Your-Emoji: Fine-Controllable and Expressive Freestyle Portrait Animation199
Follow-Your-Emoji, a diffusion-based framework for portrait animation, which animates a reference portrait with target landmark sequences, is presented and a facial fine-grained loss is proposed to improve the model’s ability of subtle expression perception and reference portrait appearance reconstruction.
- One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer186
This work designs a one-stage pipeline for expressive whole-body mesh recovery, named OSX, without separate networks for each part, and designs a Component Aware Transformer (CAT) composed of a global body encoder and a local face/hand decoder.
- Learning Skeletal Graph Neural Networks for Hard 3D Pose Estimation170
This work proposes a hop-aware hierarchical channel-squeezing fusion layer to effectively extract relevant information from neighboring nodes while suppressing undesired noises in GNN learning and proposes a temporal-aware dynamic graph construction procedure that is robust and effective for 3D pose estimation.
- From Knowledge Distillation to Self-Knowledge Distillation: A Unified Approach with Normalized Loss and Customized Soft Labels166
This work unifies the formulations of the two tasks by decomposing and reorganizing the generic KD loss into a Normalized KD (NKD) loss and customized soft labels for both target class (image’s category) and non-target classes named Universal Self-KD (USKD).
- Direct Inversion: Boosting Diffusion-based Editing with 3 Lines of Code163
Direct Inversion, a novel technique achieving optimal performance of both branches with just three lines of code, is introduced, which not only yields superior performance across 8 editing methods but also achieves nearly an order of speed-up.
- HumanSD: A Native Skeleton-Guided Diffusion Model for Human Image Generation155
Experimental results show that HumanSD outperforms ControlNet in terms of pose control and image quality, particularly when the given skeleton guidance is sophisticated, particularly when the given skeleton guidance is sophisticated.
- Lite DETR : An Interleaved Multi-Scale Encoder for Efficient DETR154
Lite DETR is presented, a simple yet efficient end-to-end object detection framework that can effectively reduce the GFLOPs of the detection head by 60% while keeping 99% of the original performance, and the efficient encoder strategy can generalize well across existing DETR-based models.
- HumanTOMATO: Text-aligned Whole-body Motion Generation138
This work proposes a Text-aligned whOle-body Motion generATiOn framework, named HumanTOMATO, which is the first attempt to the authors' knowledge towards applicable holistic motion generation in this research area and has significant advantages in both the quality of generated motions and their alignment with text.
- SmoothNet: A Plug-and-Play Network for Refining Human Poses in Videos136
SmoothNet models the natural smoothness characteristics in body movements by learning the long-range temporal relations of every joint without considering the noisy correlations among joints, and improves the temporal smoothness of existing pose estimators significantly and enhances the estimation accuracy of those challenging frames as a side-effect.
- DiffSHEG: A Diffusion-Based Approach for Real-Time Speech-Driven Holistic 3D Expression and Gesture Generation104
This work proposes DiffSHEG, a Diffusion-based approach for Speech-driven Holistic 3D Expression and Gesture generation with arbitrary length, and introduces an outpainting-based sampling strategy for arbitrary long sequence generation in diffusion models, offering flexibility and computational efficiency.
- Human-Art: A Versatile Human-Centric Dataset Bridging Natural and Artificial Scenes99
The Human-Art dataset is introduced and contains 50k high-quality images with over 123k person instances from 5 natural and 15 artificial scenarios, which are annotated with bounding boxes, keypoints, self-contact points, and text information for humans represented in both 2D and 3D.
- 98
- Explicit Box Detection Unifies End-to-End Multi-Person Pose Estimation94
This paper presents a novel end-to-end framework with Explicit box Detection for multi-person Pose estimation, called ED-Pose, where it unifies the contextual learning between human-level and keypoint-level information and surpasses heatmap-based Top-down methods under the same backbone.
- 94
- GPAvatar: Generalizable and Precise Head Avatar from Image(s)89
A framework named GPAvatar is proposed that reconstructs 3D head avatars from one or several images in a single forward pass and introduces a dynamic point-based expression field driven by a point cloud to precisely and effectively capture expressions.
- DeepFuse: An IMU-Aware Network for Real-Time 3D Human Pose Estimation from Multi-View Image78
A two-stage fully 3D network to estimate human pose in 3D space by fusing body-worn Inertial Measurement Unit (IMU) data and multi-view images deeply and introduces an IMU-bone layer to fuse the IMU and vision data earlier at data level is proposed.
- IDOL: Instant Photorealistic 3D Human Creation from a Single Image71
A scalable feed-forward transformer model is developed to predict a 3D human Gaussian representation in a uniform space from a given human image, which demonstrates the ability to efficiently reconstruct photorealistic humans at 1K resolution from a single input image using a single GPU instantly.
- AiOS: All-in-One-Stage Expressive Human Pose and Shape Estimation62
A novel ali-in-one-stage framework, AiOS, for multiple expressive human pose and shape recovery without an additional human detection step is introduced, built upon DETR, which treats multi-person whole-body mesh recovery task as a progressive set prediction problem with various sequential detection.
- DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation61
DeciWatch introduces a simple yet effective sample-denoise-recover framework that only watches sparsely sampled frames, taking advantage of the continuity of human motions and the lightweight pose representation.
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 2026-10-15. 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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