arXiv Computer Vision
HuC-VideoMAE: Human-Centric Video Masked Autoencoding from synthetic data
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arXiv AI
Sep 30
WeLike2Party! In-Context Motion Transfer for Multi-Human Image Animation
arXiv:2609.36937v1 Announce Type: cross Abstract: Human image animation aims to transfer motion from a driving video to subjects in a reference image. Despite remarkable progress in video generation,...
arXiv Computer Vision
Sep 4
BooM-VVT: Boosting Mask-Free Video Virtual Try-On with Image-Level Pseudo Data
BooM‑VVT is a mask‑free video virtual try‑on framework that builds on a keyframe‑driven paradigm. It introduces a multi‑stage training strategy using image‑level pseudo data to learn mask‑free localization, a garment‑sensitive keyframe sampling method to capture garment appearance, and a Frame‑Shared 3D‑RoPE module to align keyframes with target video frames for accurate garment detail transfer. The authors also release OmniView, a large‑scale multi‑view try‑on dataset, and demonstrate that BooM‑VVT outperforms existing methods in temporal consistency and garment fidelity.
Hugging Face Trending Papers
Sep 3
BooM-VVT: Boosting Mask-Free Video Virtual Try-On with Image-Level Pseudo Data
BooM-VVT is a mask‑free video virtual try‑on framework that builds on a keyframe‑driven paradigm. It uses a multi‑stage training strategy with image‑level pseudo data to learn mask‑free localization, introduces Garment‑Sensitive Keyframe Sampling to capture garment appearance, and employs Frame‑Shared 3D‑RoPE for spatiotemporal correspondence. The authors also create the OmniView dataset to support diverse camera viewpoints and tasks, achieving superior temporal consistency and garment fidelity compared to existing methods.
arXiv AI
Jun 30
MotionAtlas: Detailed Region Captioning for Motion-Centric Videos
arXiv:2606. 29531v1 Announce Type: cross Abstract: We propose MotionAtlas, a system for detailed captioning of motion-centric videos, comprising (1) a dedicated human-annotated benchmark, (2) a scalable, high-quality pipeline to construct training samples, and (3) a family of powerful Video-MLLMs.
arXiv Computer Vision
Oct 1
Image Classifiers are Efficient Self-Supervised Video Representation Learners
arXiv:2609.40347v1 Announce Type: new Abstract: We introduce VideoMSN, a Masked Siamese Network framework for efficient self-supervised spatio-temporal representation learning in videos. Instead of r...
arXiv AI
Aug 6
Robust and Efficient Motion Reasoning for Privacy-Aware Classroom Incident Recognition
arXiv:2608. 05115v1 Announce Type: cross Abstract: Can computer vision help make classrooms safer?