11 papers
LibraGen: Playing a Balance Game in Subject-Driven Video Generation
Jiahao Zhu, Shanshan Lao, Lijie Liu +10
With the advancement of video generation foundation models (VGFMs), customized generation, particularly subject-to-video (S2V), has attracted growing attention. However, a key chal…
DreaMontage: Arbitrary Frame-Guided One-Shot Video Generation
Jiawei Liu, Junqiao Li, Jiangfan Deng +11
The "one-shot" technique represents a distinct and sophisticated aesthetic in filmmaking. However, its practical realization is often hindered by prohibitive costs and complex real…
OmniInsert: Mask-Free Video Insertion of Any Reference via Diffusion Transformer Models
Jinshu Chen, Xinghui Li, Xu Bai +8
Recent advances in video insertion based on diffusion models are impressive. However, existing methods rely on complex control signals but struggle with subject consistency, limiti…
HuMo: Human-Centric Video Generation via Collaborative Multi-Modal Conditioning
Liyang Chen, Tianxiang Ma, Jiawei Liu +7
Human-Centric Video Generation (HCVG) methods seek to synthesize human videos from multimodal inputs, including text, image, and audio. Existing methods struggle to effectively coo…
Phantom-Data : Towards a General Subject-Consistent Video Generation Dataset
Zhuowei Chen, Bingchuan Li, Tianxiang Ma +8
Subject-to-video generation has witnessed substantial progress in recent years. However, existing models still face significant challenges in faithfully following textual instructi…
MaskDiT: Dual Mask-based Diffusion Transformer for Multi-Scene Long Video Generation
Tianhao Qi, Jianlong Yuan, Wanquan Feng +6
Sora has unveiled the immense potential of the Diffusion Transformer (DiT) architecture in single-scene video generation. However, the more challenging task of multi-scene video ge…
HyperLoRA: Parameter-Efficient Adaptive Generation for Portrait Synthesis
Mengtian Li, Jinshu Chen, Wanquan Feng +4
Personalized portrait synthesis, essential in domains like social entertainment, has recently made significant progress. Person-wise fine-tuning based methods, such as LoRA and Dre…
AR-Diffusion: Asynchronous Video Generation with Auto-Regressive Diffusion
Mingzhen Sun, Weining Wang, Gen Li +7
The task of video generation requires synthesizing visually realistic and temporally coherent video frames. Existing methods primarily use asynchronous auto-regressive models or sy…
AnyDressing: Customizable Multi-Garment Virtual Dressing via Latent Diffusion Models
Xinghui Li, Qichao Sun, Pengze Zhang +5
Recent advances in garment-centric image generation from text and image prompts based on diffusion models are impressive. However, existing methods lack support for various combina…
I2VControl: Disentangled and Unified Video Motion Synthesis Control
Wanquan Feng, Tianhao Qi, Jiawei Liu +7
Motion controllability is crucial in video synthesis. However, most previous methods are limited to single control types, and combining them often results in logical conflicts. In…
I2VControl-Camera: Precise Video Camera Control with Adjustable Motion Strength
Wanquan Feng, Jiawei Liu, Pengqi Tu +6
Video generation technologies are developing rapidly and have broad potential applications. Among these technologies, camera control is crucial for generating professional-quality…