arXiv AI
Jun 19
TeleMorpher: Toward Robust Simultaneous Motion-Location Editing
arXiv:2606. 19676v1 Announce Type: cross Abstract: Diffusion models have achieved remarkable success in image and video generation and editing.
Motion Attribution for Video Generation
arXiv:2601. 08828v2 Announce Type: replace-cross Abstract: Despite the rapid progress of video generation models, the role of data in influencing motion is poorly understood.
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arXiv AI
Jul 15
The TIME Machine: On The Power of Motion for Efficient Perception
arXiv:2605. 23045v2 Announce Type: replace-cross Abstract: Video representation learning has seen tremendous progress in recent years.
arXiv Machine Learning
Jun 30
Your Data Manifold is Secretly a Reward Model: Shell-LCC for Text-to-Video Generation
arXiv:2606. 30248v1 Announce Type: cross Abstract: Recent text-to-video (T2V) diffusion models rely heavily on auxiliary reward signals (e.
arXiv AI
Aug 11
EchoStyle: Unlocking High-Fidelity Video Stylization with Reverse Data Synthesis
arXiv:2606. 25465v2 Announce Type: replace-cross Abstract: While image stylization has been studied extensively, video stylization remains a critical and largely unsolved challenge in the field of intelligent content creation.
arXiv AI
Jun 8
MotionEnhancer: Leveraging Video Diffusion for Motion-Enhanced Vision-Language Models
arXiv:2606. 06853v1 Announce Type: cross Abstract: The new era has witnessed a remarkable capability to extend Vision-Language Models (VLMs) for tackling tasks of video understanding.
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 AI
Jun 26
Disco-LoRA: Disentangled Composition of Content, Style, and Motion for Multi-concept Video Customization
arXiv:2606. 26668v1 Announce Type: cross Abstract: Video customization based on Text-to-Video (T2V) models aims to learn specific features from reference data to generate controllable videos.
arXiv AI
Jun 2
Knowledge-Intensive Video Generation
arXiv:2606. 01285v1 Announce Type: cross Abstract: Text-to-video generation has advanced rapidly in visual quality, but remains under-evaluated for factuality and practical usefulness.
arXiv AI
Jul 8
Prompt-Adapter Context Routing for Parameter-Efficient Multi-Shot Long Video Extrapolation
arXiv:2607. 06481v1 Announce Type: cross Abstract: We present PACR-Video, a parameter-efficient framework for multi-shot long video extrapolation that preserves recurring entities, scene structure, visual style, and causal progression without full generator fine-tuning.
arXiv Machine Learning
Jul 9
Video2Reaction: Mapping Video to Audience Reaction Distribution in the Wild
arXiv:2607. 06875v1 Announce Type: cross Abstract: Understanding and forecasting audience reactions to video content are crucial for improving content creation, recommendation systems, and media analysis.
arXiv Machine Learning
Jun 9
MilliVid: Hierarchical Latents for Long-Range Consistency in Video Generation
arXiv:2606. 09056v1 Announce Type: cross Abstract: Video generative models have become increasingly powerful, but long-range consistency remains challenging to achieve because even a few dozen frames require impractically long transformer sequence lengths.
arXiv AI
Aug 7
UniVVT: A Unified End-to-End Framework for High-Fidelity Video Virtual Try-on
arXiv:2608. 05745v1 Announce Type: cross Abstract: Video Virtual Try-On (VVT) synthesizes a video of a person wearing a target garment while preserving identity, motion, and scene dynamics.