arXiv Computer Vision
Dancing Stick Figures: An Introductory Dataset for Training Video Generation Models
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arXiv AI
Oct 21
Joints, Camera, Action! Co-Generating 3D Motion and Video for Arbitrary Rigs
arXiv:2610.28632v1 Announce Type: cross Abstract: Video generation models can now depict remarkably realistic motion. Yet it is unclear how to use this motion beyond pixels: to animate a specific 3D...
arXiv Computer Vision
Sep 10
Visko Orbis 1.0: A Live Model for Real-Time Interactive Long Video Generation
arXiv:2607.26694v3 Announce Type: replace Abstract: We present Visko Orbis 1.0, a Live Model for real-time, interactive long video generation. Users can change the prompt at any moment during generat...
arXiv Computer Vision
Oct 9
Stop My Dancing! Understanding, Detecting and Attributing Motion-Aware Deepfake Videos
The paper introduces Motion‑Aware Deepfake (MAD) videos, a new class of deepfakes generated by pose‑guided diffusion models that can animate entire human figures. To study this threat, the authors build the first MAD‑specific benchmark with 1.5 million frames from 1,363 real and 30,122 synthetic videos, and analyze the subtle high‑frequency artifacts and spectral fingerprints that arise from limited input frames. Leveraging these insights, they propose MoDA, a defense framework that combines spatial semantics with steganalysis‑rich frequency features, achieving high detection and attribution accuracy across in‑distribution, cross‑dataset, and zero‑shot scenarios, and demonstrating robustness against adaptive attacks.
Hugging Face Trending Papers
Jun 29
OmniDance: Multimodal Driven Dance Video Generation with Large-scale Internet Data
Music-driven dance video generation aims to synthesize expressive human motion that is temporally aligned with music while maintaining high visual fidelity. Despite recent progress, existing methods still face two key limitations: the lack of large-scale, high-quality dance video datasets, and the absence of principled frameworks for integrating music as a complementary conditioning signal into Video Generation Foundation Models.
Hugging Face Trending Papers
Oct 2
FlowHMR: Physically Plausible Motion Capture from Video
We present FlowHMR, a framework for recovering physically plausible global 3D human motion from monocular video. Previous learning-based methods typically regress human motion directly from video and...
arXiv Computer Vision
Oct 5
FlowHMR: Physically Plausible Motion Capture from Video
arXiv:2610.03691v1 Announce Type: new Abstract: We present FlowHMR, a framework for recovering physically plausible global 3D human motion from monocular video. Previous learning-based methods typica...