11 papers · 1 filter
HandsOnWorld: Unconstrained Egocentric Video Generation with Camera-Disentangled Hand Control
Yushuo Chen, Xiaoyu Shi, Xiaoshi Wu +3
We present HandsOnWorld, a framework for hand-controlled egocentric video generation that learns directly from unconstrained monocular video. Prior generators depend on 3D hand ann…
DecMem: Towards Minute-Long Consistent World Generation with Decoupled Memory
Zhenhao Yang, Xiaoshi Wu, Zhengyao Lv +5
Recent advances in video generative models have promoted rapid progress in controllable world models. However, maintaining fine-grained spatio-temporal consistency under long-horiz…
Less Gaussians, Texture More: 4K Feed-Forward Textured Splatting
Yixing Lao, Xuyang Bai, Xiaoyang Wu +7
Existing feed-forward 3D Gaussian Splatting methods predict pixel-aligned primitives, leading to a quadratic growth in primitive count as resolution increases. This fundamentally l…
SemanticGen: Video Generation in Semantic Space
Jianhong Bai, Xiaoshi Wu, Xintao Wang +9
State-of-the-art video generative models typically learn the distribution of video latents in the VAE space and map them to pixels using a VAE decoder. While this approach can gene…
KlingAvatar 2.0 Technical Report
Kling Team, Jialu Chen, Yikang Ding +25
Avatar video generation models have achieved remarkable progress in recent years. However, prior work exhibits limited efficiency in generating long-duration high-resolution videos…
MultiShotMaster: A Controllable Multi-Shot Video Generation Framework
Qinghe Wang, Xiaoyu Shi, Baolu Li +7
Current video generation techniques excel at single-shot clips but struggle to produce narrative multi-shot videos, which require flexible shot arrangement, coherent narrative, and…
RelightMaster: Precise Video Relighting with Multi-plane Light Images
Weikang Bian, Xiaoyu Shi, Zhaoyang Huang +6
Recent advances in diffusion models enable high-quality video generation and editing, but precise relighting with consistent video contents, which is critical for shaping scene atm…
VFXMaster: Unlocking Dynamic Visual Effect Generation via In-Context Learning
Baolu Li, Yiming Zhang, Qinghe Wang +8
Visual effects (VFX) are crucial to the expressive power of digital media, yet their creation remains a major challenge for generative AI. Prevailing methods often rely on the one-…
CamCloneMaster: Enabling Reference-based Camera Control for Video Generation
Yawen Luo, Jianhong Bai, Xiaoyu Shi +6
Camera control is crucial for generating expressive and cinematic videos. Existing methods rely on explicit sequences of camera parameters as control conditions, which can be cumbe…
CineMaster: A 3D-Aware and Controllable Framework for Cinematic Text-to-Video Generation
Qinghe Wang, Yawen Luo, Xiaoyu Shi +7
In this work, we present CineMaster, a novel framework for 3D-aware and controllable text-to-video generation. Our goal is to empower users with comparable controllability as profe…
3DTrajMaster: Mastering 3D Trajectory for Multi-Entity Motion in Video Generation
Xiao Fu, Xian Liu, Xintao Wang +7
This paper aims to manipulate multi-entity 3D motions in video generation. Previous methods on controllable video generation primarily leverage 2D control signals to manipulate obj…