23 citations · 36 across the 22 of their papers we have counts for
21 papers · 1 filter
VicEdit: Learning to Edit Videos from Visual In-Context Examples
Yuji Wang, Teng Hu, Yuheng Chen +6
Despite progress in instruction-based video editing, unimodal textual instructions inherently struggle to convey fine-grained textures and complex dynamics. To bridge this perceptu…
PersonaShot: Benchmarking Person-Centric Narrative Continuity in Multi-Shot Video Generation
Yuji Wang, Yuheng Chen, Teng Hu +7
Video generation is rapidly evolving from single-shot clips to multi-shot narratives, where the human character serves as the core narrative anchor. However, existing benchmarks ma…
Efficient Audio-Visual Generation via Synchrony-Aware Cross-Modal Sparse Attention
Shengchuan Gao, Teng Hu, Bohao Feng +4
Recent audio-visual generation models can synthesize synchronized video and sound in a unified diffusion process, but their inference cost remains high because long video token seq…
Cycle-World: Mitigating Error Accumulation in Long-term Video World Models via Reverse-Prediction Cycle Consistency
Zihan Su, Teng Hu, Jiangning Zhang +4
Autoregressive diffusion models have enabled high-quality video generation, yet their sequential nature inherently suffers from error accumulation. In long-horizon video synthesis,…
MetaWorld: Scaling Multi-Agent Video World Model from Single-view Video Data
Teng Hu, Mingchun Lu, Yating Wang +6
Video world models are a foundational generative technology for embodied AI and the Metaverse, yet existing approaches are inherently limited to a single agent observing from a sin…
Transform Trained Transformer for Accelerating Native 4K Video Generation
Jiangning Zhang, Junwei Zhu, Teng Hu +9
Native 4K (21763840) video generation remains a critical challenge due to the quadratic computational explosion of full-attention as spatiotemporal resolution increases, ma…
UltraGen: High-Resolution Video Generation with Hierarchical Attention
Teng Hu, Jiangning Zhang, Zihan Su +1
Recent advances in video generation have made it possible to produce visually compelling videos, with wide-ranging applications in content creation, entertainment, and virtual real…
IVEBench: Modern Benchmark Suite for Instruction-Guided Video Editing Assessment
Yinan Chen, Jiangning Zhang, Teng Hu +7
Instruction-guided video editing has emerged as a rapidly advancing research direction, offering new opportunities for intuitive content transformation while also posing significan…
UltraVideo: High-Quality UHD Video Dataset with Comprehensive Captions
Zhucun Xue, Jiangning Zhang, Teng Hu +8
The quality of the video dataset (image quality, resolution, and fine-grained caption) greatly influences the performance of the video generation model. The growing demand for vide…
Image Inversion: A Survey from GANs to Diffusion and Beyond
Yinan Chen, Jiangning Zhang, Yali Bi +6
Image inversion is a fundamental task in generative models, aiming to map images back to their latent representations to enable downstream applications such as editing, restoration…
Improving Autoregressive Visual Generation with Cluster-Oriented Token Prediction
Teng Hu, Jiangning Zhang, Ran Yi +5
Employing LLMs for visual generation has recently become a research focus. However, the existing methods primarily transfer the LLM architecture to visual generation but rarely inv…
Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration
Yuzhen Du, Teng Hu, Jiangning Zhang +6
Image restoration (IR) aims to recover high-quality images from degraded inputs, with recent deep learning advancements significantly enhancing performance. However, existing metho…
EMOv2: Pushing 5M Vision Model Frontier
Jiangning Zhang, Teng Hu, Haoyang He +6
This work focuses on developing parameter-efficient and lightweight models for dense predictions while trading off parameters, FLOPs, and performance. Our goal is to set up the new…
SaRA: High-Efficient Diffusion Model Fine-tuning with Progressive Sparse Low-Rank Adaptation
Teng Hu, Jiangning Zhang, Ran Yi +3
In recent years, the development of diffusion models has led to significant progress in image and video generation tasks, with pre-trained models like the Stable Diffusion series p…
Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generation
Ying Jin, Jinlong Peng, Qingdong He +8
The performance of anomaly inspection in industrial manufacturing is constrained by the scarcity of anomaly data. To overcome this challenge, researchers have started employing ano…
SuperSVG: Superpixel-based Scalable Vector Graphics Synthesis
Teng Hu, Ran Yi, Baihong Qian +3
SVG (Scalable Vector Graphics) is a widely used graphics format that possesses excellent scalability and editability. Image vectorization, which aims to convert raster images to SV…
MotionMaster: Training-free Camera Motion Transfer For Video Generation
Teng Hu, Jiangning Zhang, Ran Yi +5
The emergence of diffusion models has greatly propelled the progress in image and video generation. Recently, some efforts have been made in controllable video generation, includin…
SAMVG: A Multi-stage Image Vectorization Model with the Segment-Anything Model
Haokun Zhu, Juang Ian Chong, Teng Hu +3
Vector graphics are widely used in graphical designs and have received more and more attention. However, unlike raster images which can be easily obtained, acquiring high-quality v…
AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model
Teng Hu, Jiangning Zhang, Ran Yi +5
Anomaly inspection plays an important role in industrial manufacture. Existing anomaly inspection methods are limited in their performance due to insufficient anomaly data. Althoug…
Stroke-based Neural Painting and Stylization with Dynamically Predicted Painting Region
Teng Hu, Ran Yi, Haokun Zhu +5
Stroke-based rendering aims to recreate an image with a set of strokes. Most existing methods render complex images using an uniform-block-dividing strategy, which leads to boundar…
Phasic Content Fusing Diffusion Model with Directional Distribution Consistency for Few-Shot Model Adaption
Teng Hu, Jiangning Zhang, Liang Liu +7
Training a generative model with limited number of samples is a challenging task. Current methods primarily rely on few-shot model adaption to train the network. However, in scenar…