arXiv Computer Vision
Tri-Prompting: Video Diffusion with Unified Control over Scene, Subject, and Motion
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
Hugging Face Trending Papers
Aug 24
Direct, Parallel, or Sequential? A Comparative Study of Training-Free Multi-Subject Image-to-Video Generation
Text-conditioned image-to-video (I2V) generation has advanced rapidly, yet generating videos with multiple subjects remains challenging. A model must simultaneously preserve the appearance of each sub...
arXiv Computer Vision
Aug 25
BulletTime: Decoupled Control of Time and Camera Pose for Video Generation
BulletTime presents a 4D‑controllable video diffusion framework that separates scene dynamics from camera pose, allowing precise manipulation of both temporal and spatial aspects of generated videos. The model conditions on continuous world‑time sequences and camera trajectories, integrating them via a 4D positional encoding in the attention layer and adaptive normalizations for feature modulation. A specially curated dataset with independently parameterized temporal and camera variations is used for training, and the resulting system demonstrates robust real‑world 4D control while maintaining high generation quality and surpassing prior methods in controllability.
arXiv AI
Jul 21
ReViV: Reconstructing the Viewer and the View in 4D from Monocular Egocentric Video
arXiv:2607. 17790v1 Announce Type: cross Abstract: Egocentric devices, such as wearable front-facing cameras, provide a unique perspective for capturing the continuous interaction between a human viewer and the surrounding environment.
arXiv Computer Vision
Aug 27
4DStreamCtrl: Interactive Video Generation with Online 4D Control
The paper introduces 4DStreamCtrl, a system that unifies camera motion, object trajectories, and depth into a single 3D point‑track representation, enabling joint control, depth editing, and motion transfer in a single forward pass. By mining in‑the‑wild video for 3D motion supervision and encoding it with a lightweight Geometric Motion Head, the authors train a causal streaming student that can generate arbitrarily long videos in just four denoising steps, achieving 20 FPS on a single high‑end GPU for 480p video. This approach outperforms prior camera‑only, 2D, and offline‑3D methods in motion‑control precision while maintaining temporal coherence over hundreds of frames, thereby enabling interactive 4D‑controllable streaming generation for the first time.
arXiv Computer Vision
Aug 21
HarmoHOI: Harmonizing Appearance and 3D Motion for Multi-view Hand-Object Interaction Synthesis
arXiv:2607. 17097v2 Announce Type: replace Abstract: Hand-Object Interaction (HOI) synthesis is a cornerstone for animation production and embodied AI.
arXiv AI
Oct 1
PISCO: Precise Video Instance Insertion with Sparse Control
arXiv:2602.08277v3 Announce Type: replace-cross Abstract: The landscape of AI video generation is undergoing a pivotal shift: moving beyond general generation - which relies on exhaustive prompt-engi...