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Zehong Shen

7 accepted papers

2026

Natural Human Motion Recovery by Aligning High-Order Temporal Dynamics from Monocular Videos

CVPR 2026

Human motion recovered from monocular videos often appears overly smooth or dynamically inconsistent, even when joint positions are numerically accurate. We observe that this limitation stems from the absence of reliable high-order temporal cues--velocity and acceleration--which are essential for re

Cited by 0SourceScholar
2025

Motion-2-to-3: Leveraging 2D Motion Data for 3D Motion Generations

ICCV 2025poster

Text-driven human motion synthesis has showcased its potential for revolutionizing motion design in the movie and game industry.Existing methods often rely on 3D motion capture data, which requires special setups, resulting in high costs for data acquisition, ultimately limiting the diversity and sc…

Cited by 0SourcePDFScholar
2024

Generating Human Motion in 3D Scenes from Text Descriptions

CVPR 2024poster

Generating human motions from textual descriptions has gained growing research interest due to its wide range of applications. However only a few works consider human-scene interactions together with text conditions which is crucial for visual and physical realism. This paper focuses on the task of…

2023

Learning Human Mesh Recovery in 3D Scenes

CVPR 2023poster

We present a novel method for recovering the absolute pose and shape of a human in a pre-scanned scene given a single image. Unlike previous methods that perform sceneaware mesh optimization, we propose to first estimate absolute position and dense scene contacts with a sparse 3D CNN, and later enha…

2023

Long-Term Visual Localization With Mobile Sensors

CVPR 2023poster

Despite the remarkable advances in image matching and pose estimation, image-based localization of a camera in a temporally-varying outdoor environment is still a challenging problem due to huge appearance disparity between query and reference images caused by illumination, seasonal and structural c…

2021

LoFTR: Detector-Free Local Feature Matching With Transformers

CVPR 2021poster

We present a novel method for local image feature matching. Instead of performing image feature detection, description, and matching sequentially, we propose to first establish pixel-wise dense matches at a coarse level and later refine the good matches at a fine level. In contrast to dense methods…

Cited by 1526PDFcodeScholar
2019

GIFT: Learning Transformation-Invariant Dense Visual Descriptors via Group CNNs

NeurIPS 2019poster

Finding local correspondences between images with different viewpoints requires local descriptors that are robust against geometric transformations. An approach for transformation invariance is to integrate out the transformations by pooling the features extracted from transformed versions of an ima…

Cited by 109SourcePDFScholar